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Researcher profile

Xiaodan Wang

Xiaodan Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceDatabasesDistributed, Parallel, and Cluster ComputingInformation Retrievalphysics.opticsSocial and Information Networks

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Published work

4 published item(s)

preprint2026arXiv

Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies

The proliferation of online social networks has significantly reshaped the way individuals access and engage with information. While these platforms offer unprecedented connectivity, they may foster environments where users are increasingly exposed to homogeneous content and like-minded interactions. Such dynamics are associated with selective exposure and the emergence of filter bubbles, echo chambers, tunnel vision, and polarization, which together can contribute to ideological isolation and raise concerns about information diversity and public discourse. This survey provides a comprehensive computational review of existing studies that define, analyze, quantify, and mitigate ideological isolation in online social networks. We examine the mechanisms underlying content personalization, user behavior patterns, and network structures that reinforce content-exposure concentration and narrowing dynamics. This paper also systematically reviews methodological approaches for detecting and measuring these isolation-related phenomena, covering network-, content-, and behavior-based metrics. We further organize computational mitigation strategies, including network-topological interventions and recommendation-level controls, and discuss their trade-offs and deployment considerations. By integrating definitions, metrics, and interventions across structural/topological, content-based, interactional, and cognitive isolation, this survey provides a unified computational framework. It serves as a reference for understanding and addressing the key challenges and opportunities in promoting information diversity and reducing ideological fragmentation in the digital age.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation

Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user interests evolve over time and exhibit nonstationary dynamics, where different preference factors change at different rates. This challenge is amplified in multimodal settings because visual and textual cues can dominate decisions under different temporal regimes. Despite strong progress, most multimodal recommenders still rely on static interaction graphs or coarse temporal heuristics, which limits their ability to model continuous preference evolution with fine-grained temporal adaptation. To address these limitations, we propose TimeMM, a time-conditioned spectral filtering framework for dynamic multimodal recommendation. TimeMM instantiates Time-as-Operator by mapping interaction recency to a family of parametric temporal kernels that reweight edges on the user--item graph, producing component-specific representations without explicit eigendecomposition. To capture non-stationary interests, we introduce Adaptive Spectral Filtering that mixes the operator bank according to temporal context, yielding prediction-specific effective spectral responses. To account for modality-specific temporal sensitivity, we further propose Spectral-Aware Modality Routing that calibrates visual and textual contributions conditioned on the same temporal context. Finally, a ranking-space Spectral Diversity Regularization encourages complementary expert behaviors and prevents filter-bank collapse. Extensive experiments on real-world benchmarks demonstrate that TimeMM consistently outperforms state-of-the-art multimodal recommenders while maintaining linear-time scalability.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quadruple-enhanced four-wave mixing in nanometer plasmonic hotspots: classical theory and experiments

Efficiency is a critical factor limiting the applications of nonlinear plasmonic devices. We show by theory and experiments that high efficiency four-wave mixing (FWM) is achieved in nanometer size plasmonic hotspots, which open up opportunities for nanoscale light manipulation. First, we present a classical calculation on the efficiency of frequency conversion by quadruple-enhanced FWM for a Kerr nonlinear material loaded in the plasmonic hotspot of a gold nanosphere dimer. The results indicate the viability to achieve over 10% efficiency in a nanometer volume under milliwatts of pump power consumption or less. Next, we present experimental results which show around 10% linewidth broadening of a 100 fs pulsed laser by a monolayer graphene in a gold nanosphere-plane junction. Such a high efficiency, low power, and nanoscale nonlinear process is a promising candidate for making ultra-compact and high-speed nonlinear optical devices.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

A Dynamic Data Middleware Cache for Rapidly-growing Scientific Repositories

Modern scientific repositories are growing rapidly in size. Scientists are increasingly interested in viewing the latest data as part of query results. Current scientific middleware cache systems, however, assume repositories are static. Thus, they cannot answer scientific queries with the latest data. The queries, instead, are routed to the repository until data at the cache is refreshed. In data-intensive scientific disciplines, such as astronomy, indiscriminate query routing or data refreshing often results in runaway network costs. This severely affects the performance and scalability of the repositories and makes poor use of the cache system. We present Delta, a dynamic data middleware cache system for rapidly-growing scientific repositories. Delta's key component is a decision framework that adaptively decouples data objects---choosing to keep some data object at the cache, when they are heavily queried, and keeping some data objects at the repository, when they are heavily updated. Our algorithm profiles incoming workload to search for optimal data decoupling that reduces network costs. It leverages formal concepts from the network flow problem, and is robust to evolving scientific workloads. We evaluate the efficacy of Delta, through a prototype implementation, by running query traces collected from a real astronomy survey.

0 citations0 reviewsNo code linkedOpen access