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Wenjie Zhang

Wenjie Zhang contributes to research discovery and scholarly infrastructure.

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preprint2026arXiv

Anchor-guided Hypergraph Condensation with Dual-level Discrimination

The increasing prevalence of large-scale hypergraphs poses significant computational challenges for hypergraph neural network (HNN) training. To address this, hypergraph condensation (HGC) distills large real hypergraphs into compact yet informative synthetic ones, beyond graph condensation (GC) methods limited to pairwise relations. However, existing HGC methods rely on decoupled training architectures, where structure generators are pre-trained on the original hypergraph but not jointly optimized with condensed features during refinement, resulting in misaligned structures that degrade downstream utility. Moreover, trajectory-based optimization incurs substantial computational overhead in refinement, limiting condensation efficiency. To tackle these issues, we propose \textbf{A}nchor-guided \textbf{H}yper\textbf{G}raph \textbf{C}ondensation with \textbf{D}ual-level \textbf{D}iscrimination (\textbf{AHGCDD}), which consists of three key components: (1) a node initialization module based on Heat Kernel PageRank (HKPR) to encode structural knowledge into feature semantics; (2) an anchor-guided hyperedge synthesis strategy for joint optimization of condensed features and structure; (3) a theoretically grounded dual-level discrimination objective for utility-preserving condensation without redundant HNN training. Extensive experiments demonstrate the superior effectiveness and efficiency of AHGCDD.

preprint2026arXiv

Discovery of a soft X-ray lag in the tidal disruption event AT2021ehb

In this Letter, we report the detection of soft X-ray time lags-i.e. variability in the softer photons lagging behind that in the harder photons-in seven XMM-Newton observations of the tidal disruption event (TDE) candidate AT2021ehb. We find correlated variability between the soft (0.3-0.7 keV) and hard (0.9-10 keV) bands on about 10^4 s time-scales, and measure a soft lag of about 500 s. This behaviour is broadly consistent with the disk-corona reverberation scenario established in active galactic nuclei (AGNs). Together with the previously reported strong hard X-ray emission and broad Fe K line, our results suggest the presence of a compact corona and prominent relativistic disk reflection in AT2021ehb. The unusually high blackbody temperature (peaking at about 200 eV) is difficult to reconcile with thermal emission from a standard accretion disk around a about 10^7 Msun black hole, and may instead be analogous to the soft excess commonly observed in AGNs, whose physical origin remains debated. Finally, the measured lags offer a possible explanation for the rapid X-ray flux decline that occurred only three days after the peak, pointing to a scenario in which the corona cools following a sudden loss of the magnetic support required to sustain it.

preprint2026arXiv

EXG: Self-Evolving Agents with Experience Graphs

Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents remain behaviorally static, with knowledge acquired during execution rarely translating into systematic improvement over time. In response, a growing line of work on self-evolving agents explores how agents can improve through experience during deployment, but most existing approaches either rely on ad hoc reflection limited to single-task correction or adopt unstructured memory that accumulates fragmented experience with delayed usability. To address this limitation, we introduce EXG, an experience graph framework for self-evolving agents that explicitly organizes accumulated successes and failures into a structured, relational representation. EXG is the first experience graph designed for self-evolving agents, supporting both online, real-time graph growth during execution for immediate cross-task experience reuse, and offline reuse of a consolidated experience graph as an external memory module. This design also enables EXG to serve as a plug-and-play component for existing self-evolving agents, organizing prior experience into a unified experience graph and improving both solution quality and resource efficiency as deployment progresses. Extensive experiments across code generation and reasoning benchmarks show that EXG attains more favorable performance-efficiency trade-offs than reflection- and memory-based baselines in both online and offline evaluations. Our results suggest that structuring experience as a graph provides a principled foundation for scalable and transferable self-evolving agent behavior.

preprint2026arXiv

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model

Conditional generative models, particularly diffusion-based methods, have recently been applied to graph prediction by modeling the target as a conditional distribution given the input graph, yielding competitive results compared to deterministic predictor. However, existing diffusion-based prediction methods typically require expensive iterative denoising at inference and often suffer from unstable sampling, which motivates recent efforts to reduce inference denoising steps and enable stable sampling via techniques such as consistency training. Despite this progress, we find that existing consistency training methods for graph prediction could potentially fall into a shortcut solution: the model may attempt to satisfy the self-consistency constraint by ignoring the noisy target (i.e., assigning it negligible weight), ultimately collapsing into a purely deterministic predictor. To mitigate such shortcut solution, we propose GCCM, a graph contrastive consistency model that goes beyond isolated pairwise matching between the same target at different noise levels by introducing negative pairs into a contrastive consistency objective. This adds an additional separation requirement, making the shortcut solution no longer trivially sufficient to satisfy the proposed objective. Moreover, we apply feature perturbation to the input node/edge features to break identical conditioning on the input graph, so that the shortcut no longer yields the same predictions across noise levels and becomes less attractive. Extensive experiments on benchmark datasets demonstrate that GCCM mitigates the shortcut solution and yields consistent performance improvements in graph prediction compared to deterministic predictors.

preprint2026arXiv

HyperJoin: LLM-augmented Hypergraph Link Prediction for Joinable Table Discovery

As a pivotal task in data lake management, joinable table discovery has attracted widespread interest. While existing language model-based methods achieve remarkable performance by combining offline column representation learning with online ranking, their design insufficiently accounts for the underlying structural interactions: (1) offline, they directly model tables into isolated or pairwise columns, thereby struggling to capture the rich inter-table and intra-table structural information; and (2) online, they rank candidate columns based solely on query-candidate similarity, ignoring the mutual interactions among the candidates, leading to incoherent result sets. To address these limitations, we propose HyperJoin, a large language model (LLM)-augmented Hypergraph framework for Joinable table discovery. Specifically, we first construct a hypergraph to model tables using both the intra-table hyperedges and the LLM-augmented inter-table hyperedges. Consequently, the task of joinable table discovery is formulated as link prediction on this constructed hypergraph. We then design HIN, a Hierarchical Interaction Network that learns expressive column representations through bidirectional message passing over columns and hyperedges. To strengthen coherence and internal consistency in the result columns, we cast online ranking as a coherence-aware top-k column selection problem. We then introduce a reranking module that leverages a maximum spanning tree algorithm to prune noisy connections and maximize coherence. Experiments demonstrate the superiority of HyperJoin, achieving average improvements of 21.4% (Precision@15) and 17.2% (Recall@15) over the best baseline.

preprint2026arXiv

Modeling Epidemic Dynamics of Mutant Strains with Evolutionary Game-based Vaccination Behavior

The outbreak of mutant strains and vaccination behaviors have been the focus of recent epidemiological research, but most existing epidemic models failed to simultaneously capture viral mutation and consider the complexity and behavioral dynamics of vaccination. To address this gap, we develop an extended SIRS model that distinguishes infections with the original strain and a mutant strain, and explicitly introduces a vaccinated compartment state. At the behavioral level, we employ evolutionary game theory to model individual vaccination decisions, where strategies are determined by both neighbors' choices and the current epidemiological situation. This process corresponds to the time-varying vaccination rate of susceptible individuals transitioning to vaccinated individuals at the epidemic spreading level. We then couple the epidemic and vaccination behavioral processes through the microscopic Markov chain approach (MMCA) and finally investigate the evolutionary dynamics via numerical simulations. The results show that our framework can effectively mitigate outbreaks across different disease scenarios. Sensitivity analysis further reveals that vaccination uptake is most strongly influenced by vaccine cost, efficacy, and perceived risk of side effects. Overall, this behavior-aware modeling framework captures the co-evolution of viral mutation and vaccination behavior, providing quantitative and theoretical support for designing effective public health vaccination policies.

preprint2026arXiv

PrivGemo: Privacy-Preserving Dual-Tower Graph Retrieval for Empowering LLM Reasoning with Memory Augmentation

Knowledge graphs (KGs) provide structured evidence that can ground large language model (LLM) reasoning for knowledge-intensive question answering. However, many practical KGs are private, and sending retrieved triples or exploration traces to closed-source LLM APIs introduces leakage risk. Existing privacy treatments focus on masking entity names, but they still face four limitations: structural leakage under semantic masking, uncontrollable remote interaction, fragile multi-hop and multi-entity reasoning, and limited experience reuse for stability and efficiency. To address these issues, we propose PrivGemo, a privacy-preserving retrieval-augmented framework for KG-grounded reasoning with memory-guided exposure control. PrivGemo uses a dual-tower design to keep raw KG knowledge local while enabling remote reasoning over an anonymized view that goes beyond name masking to limit both semantic and structural exposure. PrivGemo supports multi-hop, multi-entity reasoning by retrieving anonymized long-hop paths that connect all topic entities, while keeping grounding and verification on the local KG. A hierarchical controller and a privacy-aware experience memory further reduce unnecessary exploration and remote interactions. Comprehensive experiments on six benchmarks show that PrivGemo achieves overall state-of-the-art results, outperforming the strongest baseline by up to 17.1%. Furthermore, PrivGemo enables smaller models (e.g., Qwen3-4B) to achieve reasoning performance comparable to that of GPT-4-Turbo.

preprint2026arXiv

Revisiting Graph Analytics Benchmark

The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of API usability evaluation. To address these shortcomings, we propose a novel graph analytics benchmark. First, we select eight core algorithms by extensively reviewing both academic and industrial settings. Second, we design an efficient and flexible data generator and produce eight new synthetic datasets as the default datasets for our benchmark. Lastly, we introduce a multi-level large language model (LLM)-based framework for API usability evaluation-the first of its kind in graph analytics benchmarks. We conduct comprehensive experimental evaluations on existing platforms (GraphX, PowerGraph, Flash, Grape, Pregel+, Ligra and G-thinker). The experimental results demonstrate the superiority of our proposed benchmark.

preprint2026arXiv

TRACE: Tourism Recommendation with Accountable Citation Evidence

Tourism is a high-stakes setting for conversational recommender systems (CRS): a plausible-sounding suggestion can waste real money and trip time once a traveler acts on it. Existing CRS benchmarks primarily evaluate systems with a single Recall@k score over entity mentions, and tourism-specific resources add spatial or knowledge-graph context, yet none of them couple multi-turn recommendation with verbatim review-span evidence and rejection recovery. This leaves an evaluation gap for tourism recommendation that is simultaneously trustworthy, verifiable, and adaptive: recommend the right point of interest (POI) for multi-aspect preferences (such as cuisine, price, atmosphere, walking distance), justify each suggestion with verifiable evidence from prior visitors so the traveler can act without trial and error, and recover when the first recommendation is rejected mid-dialogue. We introduce TRACE, where each item is a multi-turn tourism recommendation dialogue with review-span citations and explicit rejection turns: 10,000 dialogues over 2,400 Yelp POIs and 34,208 reviews across eight U.S. cities, paired with 14 retrieval, planning, and LLM baselines, along with 25 metrics organized under Accuracy, Grounding, and Recovery. Across these baselines, TRACE reveals the Three-Competency Gap: LLM Zero-Shot leads in closed-set Recall@1 and rejection recovery but cites less densely than retrievers; non-LLM retrievers achieve surface-verbatim grounding but with low accuracy; Multi-Review Synthesis fails at recovery. The Grounding Score agrees with human citation precision (Spearman rho=+0.80, p<10^-20), and paired t-tests reproduce the per-baseline ranking (p<0.01 on the dominant contrasts). TRACE reframes accountable tourism recommendation as a joint target (right POI, verifiable evidence, adaptive repair) rather than a single-axis leaderboard.

preprint2024arXiv

Batch Hop-Constrained s-t Simple Path Query Processing in Large Graphs

Hop-constrained s-t simple path (HC-s-t path) enumeration is a fundamental problem in graph analysis. Existing solutions for this problem focus on optimizing the processing performance of a single query. However, in practice, it is more often that multiple HC-s-t path queries are issued simultaneously and processed as a batch. Therefore, we study the problem of batch HC-s-t path query processing in this paper and aim to compute the results of all queries concurrently and efficiently as a batch. To achieve this goal, we first propose the concept of HC-s path query which can precisely characterize the common computation among different queries.We then devise a two-phase HC-s path query detection algorithm to identify the common HC-s path queries for the given HC-s-t path queries. Based on the detected HC-s path queries, we further devise an efficient HC-s-t path enumeration algorithm in which the common computation represented by HC-s path queries are effectively shared. We conduct extensive experiments on real-world graphs and the experimental results demonstrate that our proposed algorithm is efficient and scalable regarding processing multiple HC-s-t path queries in large graphs at billion-scale.

preprint2023arXiv

iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre

The capability of generating speech with specific type of emotion is desired for many applications of human-computer interaction. Cross-speaker emotion transfer is a common approach to generating emotional speech when speech with emotion labels from target speakers is not available for model training. This paper presents a novel cross-speaker emotion transfer system, named iEmoTTS. The system is composed of an emotion encoder, a prosody predictor, and a timbre encoder. The emotion encoder extracts the identity of emotion type as well as the respective emotion intensity from the mel-spectrogram of input speech. The emotion intensity is measured by the posterior probability that the input utterance carries that emotion. The prosody predictor is used to provide prosodic features for emotion transfer. The timber encoder provides timbre-related information for the system. Unlike many other studies which focus on disentangling speaker and style factors of speech, the iEmoTTS is designed to achieve cross-speaker emotion transfer via disentanglement between prosody and timbre. Prosody is considered as the main carrier of emotion-related speech characteristics and timbre accounts for the essential characteristics for speaker identification. Zero-shot emotion transfer, meaning that speech of target speakers are not seen in model training, is also realized with iEmoTTS. Extensive experiments of subjective evaluation have been carried out. The results demonstrate the effectiveness of iEmoTTS as compared with other recently proposed systems of cross-speaker emotion transfer. It is shown that iEmoTTS can produce speech with designated emotion type and controllable emotion intensity. With appropriate information bottleneck capacity, iEmoTTS is able to effectively transfer emotion information to a new speaker. Audio samples are publicly available https://patrick-g-zhang.github.io/iemotts/

preprint2022arXiv

A Syntax-Guided Edit Decoder for Neural Program Repair

Automated Program Repair (APR) helps improve the efficiency of software development and maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder architecture, to generate patches. Though existing DL-based APR approaches have proposed different encoder architectures, the decoder remains to be the standard one, which generates a sequence of tokens one by one to replace the faulty statement. This decoder has multiple limitations: 1) allowing to generate syntactically incorrect programs, 2) inefficiently representing small edits, and 3) not being able to generate project-specific identifiers. In this paper, we propose Recoder, a syntax-guided edit decoder with placeholder generation. Recoder is novel in multiple aspects: 1) Recoder generates edits rather than modified code, allowing efficient representation of small edits; 2) Recoder is syntax-guided, with the novel provider/decider architecture to ensure the syntactic correctness of the patched program and accurate generation; 3) Recoder generates placeholders that could be instantiated as project-specific identifiers later. We conduct experiments to evaluate Recoder on 395 bugs from Defects4J v1.2 and 420 additional bugs from Defects4J v2.0. Our results show that Recoder repairs 53 bugs on Defects4J v1.2, which achieves 21.4% improvement over the previous state-of-the-art approach for single-hunk bugs (TBar). Importantly, to our knowledge, Recoder is the first DL-based APR approach that has outperformed the traditional APR approaches on this dataset. Furthermore, Recoder also repairs 19 bugs on the additional bugs from Defects4J v2.0, which is 137.5% more than TBar (8 bugs) and 850% more than SimFix (2 bugs). This result suggests that Recoder has better generalizability than existing APR approaches.

preprint2022arXiv

Balanced Clique Computation in Signed Networks: Concepts and Algorithms

Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. However, in real world, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks. Following the balanced clique model, we study the maximal balanced clique enumeration problem (MBCE) which computes all the maximal balanced cliques in a given signed network and the maximum balanced clique search problem (MBCS) which computes the balanced clique with maximum size. We show that MBCE problem and MBCS problem are both NP-Hard. For the MBCE problem, a straightforward solution is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. For the MBCS problem, we propose a new search framework based on search space partition. To further improve the efficiency of the new framework, we propose multiple optimization strategies regarding to redundant search branches and invalid candidates. We conduct extensive experiments on large real datasets. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms for MBCE problem and MBCS problem.

preprint2022arXiv

Efficient k-clique Listing with Set Intersection Speedup [Technical Report]

Listing all k-cliques is a fundamental problem in graph mining, with applications in finance, biology, and social network analysis. However, owing to the exponential growth of the search space as k increases, listing all k-cliques is algorithmically challenging. DDegree and DDegCol are the state-of-the-art algorithms that exploit ordering heuristics based on degree ordering and color ordering, respectively. Both DDegree and DDegCol induce high time and space overhead for set intersections cause they construct and maintain all induced subgraphs. Meanwhile, it is non-trivial to implement the data level parallelism to further accelerate on DDegree and DDegCol. In this paper, we propose two efficient algorithms SDegree and BitCol for k-clique listing. We mainly focus on accelerating the set intersections for k-clique listing. Both SDegree and BitCol exploit the data level parallelism for further acceleration with single instruction multiple data (SIMD) or vector instruction sets. Furthermore, we propose two preprocessing techniques Pre-Core and Pre-List, which run in linear time. The preprocessing techniques significantly reduce the size of the original graph and prevent exploring a large number of invalid nodes. In the theoretical analysis, our algorithms have a comparable time complexity and a slightly lower space complexity than the state-of-the-art algorithms. The comprehensive experiments reveal that our algorithms outperform the state-of-the-art algorithms by 3.75x for degree ordering and 5.67x for color ordering on average.

preprint2022arXiv

Generalized Equivariance and Preferential Labeling for GNN Node Classification

Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally unable to address the data with multiple possible outputs. In this paper, we analyze the limitation of existing approaches to node classification problems. Inspired by our analysis, we propose a generalized equivariance property and a Preferential Labeling technique that satisfies the desired property asymptotically. Experimental results show that we achieve high performance in several unattributed node classification tasks.

preprint2022arXiv

GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]

With the development of traffic prediction technology, spatiotemporal prediction models have attracted more and more attention from academia communities and industry. However, most existing researches focus on reducing model's prediction error but ignore the error caused by the uneven distribution of spatial events within a region. In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size. In order to solve OGSS, we analyze the upper bound of real error of spatiotemporal prediction models and minimize the real error by minimizing its upper bound. Through in-depth analysis, we find that the upper bound of real error will decrease then increase when the number of model grids increase from 1 to the maximum allowed value. Then, we propose two algorithms, namely Ternary Search and Iterative Method, to automatically find the optimal grid size. Finally, the experiments verify that the error of prediction has the same trend as its upper bound, and the change trend of the upper bound of real error with respect to the increase of the number of model grids will decrease then increase. Meanwhile, in a case study, by selecting the optimal grid size, the order dispatching results of a state-of-the-art prediction-based algorithm can be improved up to 13.6%, which shows the effectiveness of our methods on tuning the region partition for spatiotemporal prediction models.

preprint2022arXiv

Observation of Ultrafast Interfacial Exciton Formation and Recombination in Graphene/MoS2 Heterostructure

In this study,we combined time-resolved terahertz spectroscopy along with transient absorption spectroscopy to revisit the interlayer non-equilibrium carrier dynamics in largely lateral size Gr/MoS2 heterostructure fabricated with chemical vapor deposition method. Our experimental results reveal that, with photon-energy below the A-exciton of MoS2 monolayer, hot electrons transfer from graphene to MoS2 takes place in time scale of less than 0.5 ps, resulting in ultrafast formation of interfacial exciton in the heterostructure, subsequently, recombination relaxation of the interfacial exciton occurs in time scale of ~18 ps. A new model considering carrier heating and photogating effect in graphene is proposed to estimate the amount of carrier transfer in the heterostructure, which shows a good agreement with experimental result. Moreover, when the photon-energy is on-resonance with the A-exciton of MoS2, photogenerated holes in MoS2 are transferred to graphene layer within 0.5 ps, leading to the formation of interfacial exciton, the subsequent photoconductivity (PC) relaxation of graphene and bleaching recovery of A-exciton in MoS2 take place around ~10 ps time scale, ascribing to the interfacial exciton recombination. The faster recombination time of interfacial exciton with on-resonance excitation could come from the reduced interface barrier caused by bandgap renormalization effect. Our study provides deep insight into the understanding of interfacial charge transfer as well as the relaxation dynamics in graphene-based heterostructures, which are promising for the applications of graphene-based optoelectronic devices.

preprint2022arXiv

Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step.Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.

preprint2022arXiv

Semiconductor-like photocarrier dynamics in Dirac Semimetal Cd3As2 film Probed with transient Terahertz Spectroscopy

The topological three-dimensional Dirac semimetal Cd3As2 has drawn great attention for the novel physics and promising applications in optoelectronic devices operating in the infrared and THz regimes. Among the extensive studies in the past decades, one intriguing debate is the underlined mechanism that governing the nonequilibrium carrier dynamics following photoexcitation. In this study, the temperature dependent photocarrier dynamics in Cd3As2 film has been investigated with time-resolved terahertz spectroscopy. The experimental results demonstrate that photoexcitation results in abrupt increase in THz photoconductivity, and the subsequent relaxation shows a single exponential relaxation for various temperatures and pump fluences. The relaxation time increase from 4.7 ps at 5 K to 7.5 ps at 220 K, while the lifetime remains almost constant of ~7.5 ps with temperature above 220 K. A Rothwarf-Taylor model was employed to fit the temperature dependent relaxation time, and a narrow energy gap of ~35 meV is obtained, which occurs around the Dirac node. Our THz spectroscopy results demonstrate that the photocarrier relaxation in Cd3As2 shows a semiconductor-like behavior, rather than hot carrier scatterings in graphene and most of metals.

preprint2022arXiv

TDB: Breaking All Hop-Constrained Cycles in Billion-Scale Directed Graphs

The Feedback vertex set with the minimum size is one of Karp's 21 NP-complete problems targeted at breaking all the cycles in a graph. This problem is applicable to a broad variety of domains, including E-commerce networks, database systems, and program analysis. In reality, users are frequently most concerned with the hop-constrained cycles (i.e., cycles with a limited number of hops). For instance, in the E-commerce networks, the fraud detection team would discard cycles with a high number of hops since they are less relevant and grow exponentially in size. Thus, it is quite reasonable to investigate the feedback vertex set problem in the context of hop-constrained cycles, namely hop-constrained cycle cover problem. It is concerned with determining a set of vertices that covers all hop-constrained cycles in a given directed graph. A common method to solve this is to use a bottom-up algorithm, where it iteratively selects cover vertices into the result set. Based on this paradigm, the existing works mainly focus on the vertices orders and several heuristic strategies. In this paper, a totally opposite cover process topdown is proposed and bounds are presented on it. Surprisingly, both theoretical time complexity and practical performance are improved.

preprint2022arXiv

Temperature-driven Emergence of Negative Photoconductivity in Semimetal MoTe2 Film Probed with Terahertz Spectroscopy

Layered two-dimensional (2D) materials MoTe2 have been paid special attention due to the rich optoelectronic properties with various phases. The nonequilibrium carrier dynamics as well as its temperature dependence in MoTe2 are of prime importance, as it can shed light on understanding the anomalous optical response and potential applications in far infrared (IR) photodetection. Hereby, we employ time-resolved terahertz (THz) spectroscopy to study the temperature dependent nonequilibrium carrier dynamics in MoTe2 films. After photoexcitation of 1.59 eV, the 1T'-phase MoTe2 at high temperature behaves only THz positive photoconductivity (PPC) with relaxation time of less than 1 ps. In contrast, the Td-phase MoTe2 at low temperature shows ultrafast THz PPC initially followed by emerging THz negative photoconductivity (NPC), and the THz NPC signal relaxes to the equilibrium state in hundreds of ps time scale. Small polaron formation induced by hot carrier has been proposed to be ascribed to the THz NPC in the polar semimetal MoTe2 at low temperature. The polaron formation time after photoexcitation increases slightly with temperature, which is determined to be ~0.4 ps at 5 K and 0.5 ps at 100 K. Our experimental result demonstrates for the first time the dynamical formation of small poalron in MoTe2 Weyl semimetal, this is fundamental importance on the understanding the temperature dependent electron-phonon coupling and quantum phase transition, as well as the designing the MoTe2-based far IR photodetector.

preprint2022arXiv

Towards Higher-order Topological Consistency for Unsupervised Network Alignment

Network alignment task, which aims to identify corresponding nodes in different networks, is of great significance for many subsequent applications. Without the need for labeled anchor links, unsupervised alignment methods have been attracting more and more attention. However, the topological consistency assumptions defined by existing methods are generally low-order and less accurate because only the edge-indiscriminative topological pattern is considered, which is especially risky in an unsupervised setting. To reposition the focus of the alignment process from low-order to higher-order topological consistency, in this paper, we propose a fully unsupervised network alignment framework named HTC. The proposed higher-order topological consistency is formulated based on edge orbits, which is merged into the information aggregation process of a graph convolutional network so that the alignment consistencies are transformed into the similarity of node embeddings. Furthermore, the encoder is trained to be multi-orbit-aware and then be refined to identify more trusted anchor links. Node correspondence is comprehensively evaluated by integrating all different orders of consistency. {In addition to sound theoretical analysis, the superiority of the proposed method is also empirically demonstrated through extensive experimental evaluation. On three pairs of real-world datasets and two pairs of synthetic datasets, our HTC consistently outperforms a wide variety of unsupervised and supervised methods with the least or comparable time consumption. It also exhibits robustness to structural noise as a result of our multi-orbit-aware training mechanism.

preprint2022arXiv

Towards Real-Time Counting Shortest Cycles on Dynamic Graphs: A Hub Labeling Approach

With the ever-increasing prevalence of graph data in a wide spectrum of applications, it becomes essential to analyze structural trends in dynamic graphs on a continual basis. The shortest cycle is a fundamental pattern in graph analytics. In this paper, we investigate the problem of shortest cycle counting for a given vertex in dynamic graphs in light of its applicability to problems such as fraud detection. To address such queries efficiently, we propose a 2-hop labeling based algorithm called Counting Shortest Cycle (CSC for short). Additionally, techniques for dynamically updating the CSC index are explored. Comprehensive experiments are conducted to demonstrate the efficiency and effectiveness of our method. In particular, CSC enables query evaluation in a few hundreds of microseconds for graphs with millions of edges, and improves query efficiency by two orders of magnitude when compared to the baseline solutions. Also, the update algorithm could efficiently cope with edge insertions (deletions).

preprint2022arXiv

Ultrafast Dynamics of Defect-Assisted Auger process in PdSe2 films: Synergistic Interaction Between Defect Trapping and Auger Effect

Strong Coulomb interactions in two-dimensional systems, together with quantum confinement, make many-body processes particularly effective for carrier dynamics, which plays a crucial role in determining carrier lifetime, photoconductivity, and emission yield of the materials. Hereby, by using optical pump and terahertz probe spectroscopy, we have investigated the photocarrier dynamics in the PdSe2 films with different thickness. The experimental results reveal that the photocarrier relaxation consists of two components: a fast component of 2.5 ps that shows the layer-thickness independence, and a slow component has typical lifetime of 7.3 ps decreasing with the layer thickness. Surprisingly, the relaxation times for both fast and slow components are exhibited both pump fluence and temperature independence, which suggests that synergistic interactions between defect trapping and Auger effect dominate the photocarrier dynamics in PdSe2 films. A model involving defect-assisted Auger process is proposed, which can reproduce the experimental results well. The fitting results reveal that the layer dependent lifetime is determined by the defect density rather than carrier occupancy rate after photoexcitation. Our results underscore the interplay between Auger process and defects in two-dimensional semiconductors.

preprint2021arXiv

PEFP: Efficient k-hop Constrained s-t Simple Path Enumeration on FPGA

Graph plays a vital role in representing entities and their relationships in a variety of fields, such as e-commerce networks, social networks and biological networks. Given two vertices s and t, one of the fundamental problems in graph databases is to investigate the relationships between s and t. A well-studied problem in such area is k-hop constrained s-t simple path enumeration. Nevertheless, all existing algorithms targeting this problem follow the DFS-based paradigm, which cannot scale up well. Moreover, using hardware devices like FPGA to accelerate graph computation has become popular. Motivated by this, in this paper, we propose the first FPGA-based algorithm PEFP to solve the problem of k-hop constrained s-t simple path enumeration efficiently. On the host side, we propose a preprocessing algorithm Pre-BFS to reduce the graph size and search space. On the FPGA side in PEFP, we propose a novel DFS-based batching technique to save on-chip memory efficiently. In addition, we also propose caching techniques to cache necessary data in BRAM, which overcome the latency bottleneck brought by the read/write operations from/to FPGA DRAM. Finally, we propose a data separation technique to enable dataflow optimization for the path verification module; hence the sub-stages in that module can be executed in parallel. Comprehensive experiments show that PEFP outperforms the state-of-the-art algorithm JOIN by more than 1 order of magnitude by average, and up to 2 orders of magnitude in terms of preprocessing time, query processing time and total time, respectively.

preprint2020arXiv

AOT: Pushing the Efficiency Boundary of Main-memory Triangle Listing

Triangle listing is an important topic significant in many practical applications. Efficient algorithms exist for the task of triangle listing. Recent algorithms leverage an orientation framework, which can be thought of as mapping an undirected graph to a directed acylic graph, namely oriented graph, with respect to any global vertex order. In this paper, we propose an adaptive orientation technique that satisfies the orientation technique but refines it by traversing carefully based on the out-degree of the vertices in the oriented graph during the computation of triangles. Based on this adaptive orientation technique, we design a new algorithm, namely aot, to enhance the edge-iterator listing paradigm. We also make improvements to the performance of aot by exploiting the local order within the adjacent list of the vertices. We show that aot is the first work which can achieve best performance in terms of both practical performance and theoretical time complexity. Our comprehensive experiments over $16$ real-life large graphs show a superior performance of our \aot algorithm when compared against the state-of-the-art, especially for massive graphs with billions of edges. Theoretically, we show that our proposed algorithm has a time complexity of $Θ(\sum_{ \langle u,v \rangle \in \vec{E} } \min\{ deg^{+}(u),deg^{+}(v)\}))$, where $\vec{E}$ and $deg^{+}(x)$ denote the set of directed edges in an oriented graph and the out-degree of vertex $x$ respectively. As to our best knowledge, this is the best time complexity among in-memory triangle listing algorithms.

preprint2020arXiv

Efficient Bitruss Decomposition for Large-scale Bipartite Graphs

Cohesive subgraph mining in bipartite graphs becomes a popular research topic recently. An important structure k-bitruss is the maximal cohesive subgraph where each edge is contained in at least k butterflies (i.e., (2, 2)-bicliques). In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k >= 0. The existing bottom-up techniques need to iteratively peel the edges with the lowest butterfly support. In this peeling process, these techniques are time-consuming to enumerate all the supporting butterflies for each edge. To relax this issue, we first propose a novel online index -- the BE-Index which compresses butterflies into k-blooms (i.e., (2, k)-bicliques). Based on the BE-Index, the new bitruss decomposition algorithm BiT-BU is proposed, along with two batch-based optimizations, to accomplish the butterfly enumeration of the peeling process in an efficient way. Furthermore, the BiT-PC algorithm is devised which is more efficient against handling the edges with high butterfly supports. We theoretically show that our new algorithms significantly reduce the time complexities of the existing algorithms. Also, we conduct extensive experiments on real datasets and the result demonstrates that our new techniques can speed up the state-of-the-art techniques by up to two orders of magnitude.

preprint2020arXiv

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Matrix Factorization (MF) has been widely applied in machine learning and data mining. A large number of algorithms have been studied to factorize matrices. Among them, stochastic gradient descent (SGD) is a commonly used method. Heterogeneous systems with multi-core CPUs and GPUs have become more and more promising recently due to the prevalence of GPUs in general-purpose data-parallel applications. Due to the large computational cost of MF, we aim to improve the efficiency of SGD-based MF computation by utilizing the massive parallel processing power of heterogeneous multiprocessors. The main challenge in parallel SGD algorithms on heterogeneous CPU-GPU systems lies in the granularity of the matrix division and the strategy to assign tasks. We design a novel strategy to divide the matrix into a set of blocks by considering two aspects. First, we observe that the matrix should be divided nonuniformly, and relatively large blocks should be assigned to GPUs to saturate the computing power of GPUs. In addition to exploiting the characteristics of hardware, the workloads assigned to two types of hardware should be balanced. Aiming at the final division strategy, we design a cost model tailored for our problem to accurately estimate the performance of hardware on different data sizes. A dynamic scheduling policy is also used to further balance workloads in practice. Extensive experiments show that our proposed algorithm achieves high efficiency with a high quality of training quality.

preprint2020arXiv

Exploring Cohesive Subgraphs with Vertex Engagement and Tie Strength in Bipartite Graphs

We propose a novel cohesive subgraph model called $τ$-strengthened $(α,β)$-core (denoted as $(α,β)_τ$-core), which is the first to consider both tie strength and vertex engagement on bipartite graphs. An edge is a strong tie if contained in at least $τ$ butterflies ($2\times2$-bicliques). $(α,β)_τ$-core requires each vertex on the upper or lower level to have at least $α$ or $β$ strong ties, given strength level $τ$. To retrieve the vertices of $(α,β)_τ$-core optimally, we construct index $I_{α,β,τ}$ to store all $(α,β)_τ$-cores. Effective optimization techniques are proposed to improve index construction. To make our idea practical on large graphs, we propose 2D-indexes $I_{α,β}, I_{β,τ}$, and $I_{α,τ}$ that selectively store the vertices of $(α,β)_τ$-core for some $α,β$, and $τ$. The 2D-indexes are more space-efficient and require less construction time, each of which can support $(α,β)_τ$-core queries. As query efficiency depends on input parameters and the choice of 2D-index, we propose a learning-based hybrid computation paradigm by training a feed-forward neural network to predict the optimal choice of 2D-index that minimizes the query time. Extensive experiments show that ($1$) $(α,β)_τ$-core is an effective model capturing unique and important cohesive subgraphs; ($2$) the proposed techniques significantly improve the efficiency of index construction and query processing.

preprint2020arXiv

Observation of a thermoelectric Hall plateau in the extreme quantum limit

The thermoelectric Hall effect is the generation of a transverse heat current upon applying an electric field in the presence of a magnetic field. Here we demonstrate that the thermoelectric Hall conductivity $α_{xy}$ in the three-dimensional Dirac semimetal ZrTe$_5$ acquires a robust plateau in the extreme quantum limit of magnetic field. The plateau value is independent of the field strength, disorder strength, carrier concentration, or carrier sign. We explain this plateau theoretically and show that it is a unique signature of three-dimensional Dirac or Weyl electrons in the extreme quantum limit. We further find that other thermoelectric coefficients, such as the thermopower and Nernst coefficient, are greatly enhanced over their zero-field values even at relatively low fields.

preprint2020arXiv

Terahertz emission in the van der Waals magnet CrSiTe3

The van der Waals magnet CrSiTe3 (CST) has captured immense interest because it is capable of retaining the long-range ferromagnetic order even in its monolayer form, thus offering potential use in spintronic devices. Bulk CST crystal has inversion symmetry that is broken on the crystal surface. Here, by employing ultrafast terahertz (THz) emission spectroscopy and time resolved THz spectroscopy, the THz emission of the CST crystal was investigated, which shows a strong THz emission from the crystal surface under femtosecond (fs) pulse excitation at 800 nm. Theoretical analysis based on space symmetry of CST suggests the dominant role of shift current occurring on the surface with a thickness of a few quintuple layers in producing the THz emission, in consistence with the experimental observation that the emitted THz amplitude strongly depends on the azimuthal and pumping polarization angles. The present study offers a new efficient THz emitter as well as a better understanding of the nonlinear optical response of CST. It hopefully will open a window toward the investigation on the nonlinear optical response in the mono-/few-layer van der Waals crystals with low-dimensional magnetism.

preprint2020arXiv

TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes

Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks. However, limited by the representations (axis-aligned rectangles, rotated rectangles or quadrangles) adopted to describe text, existing methods may fall short when dealing with much more free-form text instances, such as curved text, which are actually very common in real-world scenarios. To tackle this problem, we propose a more flexible representation for scene text, termed as TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms. In TextSnake, a text instance is described as a sequence of ordered, overlapping disks centered at symmetric axes, each of which is associated with potentially variable radius and orientation. Such geometry attributes are estimated via a Fully Convolutional Network (FCN) model. In experiments, the text detector based on TextSnake achieves state-of-the-art or comparable performance on Total-Text and SCUT-CTW1500, the two newly published benchmarks with special emphasis on curved text in natural images, as well as the widely-used datasets ICDAR 2015 and MSRA-TD500. Specifically, TextSnake outperforms the baseline on Total-Text by more than 40% in F-measure.

Wenjie Zhang

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32 published item(s)

Anchor-guided Hypergraph Condensation with Dual-level Discrimination

Discovery of a soft X-ray lag in the tidal disruption event AT2021ehb

EXG: Self-Evolving Agents with Experience Graphs

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model

HyperJoin: LLM-augmented Hypergraph Link Prediction for Joinable Table Discovery

Modeling Epidemic Dynamics of Mutant Strains with Evolutionary Game-based Vaccination Behavior

PrivGemo: Privacy-Preserving Dual-Tower Graph Retrieval for Empowering LLM Reasoning with Memory Augmentation

Revisiting Graph Analytics Benchmark

TRACE: Tourism Recommendation with Accountable Citation Evidence

Batch Hop-Constrained s-t Simple Path Query Processing in Large Graphs

iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre

A Syntax-Guided Edit Decoder for Neural Program Repair

Balanced Clique Computation in Signed Networks: Concepts and Algorithms

Efficient k-clique Listing with Set Intersection Speedup [Technical Report]

Generalized Equivariance and Preferential Labeling for GNN Node Classification

GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]

Observation of Ultrafast Interfacial Exciton Formation and Recombination in Graphene/MoS2 Heterostructure

Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

Semiconductor-like photocarrier dynamics in Dirac Semimetal Cd3As2 film Probed with transient Terahertz Spectroscopy

TDB: Breaking All Hop-Constrained Cycles in Billion-Scale Directed Graphs

Temperature-driven Emergence of Negative Photoconductivity in Semimetal MoTe2 Film Probed with Terahertz Spectroscopy

Towards Higher-order Topological Consistency for Unsupervised Network Alignment

Towards Real-Time Counting Shortest Cycles on Dynamic Graphs: A Hub Labeling Approach

Ultrafast Dynamics of Defect-Assisted Auger process in PdSe2 films: Synergistic Interaction Between Defect Trapping and Auger Effect

PEFP: Efficient k-hop Constrained s-t Simple Path Enumeration on FPGA

AOT: Pushing the Efficiency Boundary of Main-memory Triangle Listing

Efficient Bitruss Decomposition for Large-scale Bipartite Graphs

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Exploring Cohesive Subgraphs with Vertex Engagement and Tie Strength in Bipartite Graphs

Observation of a thermoelectric Hall plateau in the extreme quantum limit

Terahertz emission in the van der Waals magnet CrSiTe3

TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes