Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 49 references
Computer Science
TL;DR
A Trust and Distrust Enhanced Recommendation (TDER) algorithm for signed social networks is proposed, which captures higher quality trust and distrust embeddings and improves recommendation performance in signed social networks.
A social relationship adjustment loss function, which dynamically adjusts the weights of social connections, and the Hilbert-Schmidt independence criterion loss function, which reduces the dependence between pre- and post-adjusted user/item embeddings, thereby amplifying the effect of adjusted social relationships on the updated embeddings.
ASRA-GNN addresses gaps in Signed Graph Neural Networks through three contributions: Sign-Aware Structural Role Attention grounded in four social network theories, a Locally Adaptive Theory Mixing gate replacing TrustSGCN's binary global threshold with a continuous per-node end-toend learned mixing function, and a Signed Contrastive Recommendation Loss providing the first ranking objective for signed user-user graphs.
Pharsana Parveen M, Stanis Arul Mary A· International journal of Com...· 0 citations
Information diffusion prediction forecasts future participants from an observed cascade prefix, enabling proactive intervention in applications such as viral marketing and misinformation mitigation. Most existing models leverage two data sources: the global social graph (exposure/trust pathways) and cascade-induced interaction relations (interest-driven co-adoption), following a ''learn-then-fuse'' pipeline that encodes both graphs with GNNs and combines them via gated fusion to condition a sequential decoder. However, we find the two views are systematically mismatched: interaction edges are largely disjoint from social links, most social neighbors never co-activate within the same cascade, and the resulting embeddings lie on near-orthogonal manifolds with negligible correspondence. With such mismatch, static fusion is ill-posed: when the views disagree, fusion enforces a compromise and can cause negative interference. We further identify three reliability mechanisms that determine when each view should be trusted: (1) behavioral consensus across views is a high-fidelity signal of influence; (2) social cues are essential in cold-start regimes where interactions are sparse and biased; and (3) social ties dominate early seeding, while interaction patterns govern the late viral stage. Motivated by these, we propose CARD, a context-aware routing framework that replaces static fusion with step-wise evidence arbitration. CARD constructs an expert pool with social and interaction experts, a consensus expert that activates when both views are confirmed to behavioral consensus, and a graph-agnostic prior expert for noisy fallback. A router hard-selects the single most reliable expert at each step, so the decoder receives a targeted signal rather than a blurred mixture. Extensive experiments on four real-world datasets show that CARD achieves state-of-the-art accuracy and stronger robustness.
Zihan Feng, Yajun Yang, Rui Wu et al.· Proceedings of the 32nd ACM...· 0 citations
Orthogonal Decomposition for Social Recommendation (ODSR) is proposed, an embedding-space framework that orthogonally decomposes the aggregated social message into an aligned component and an orthogonal deviation, and learns a dimension-wise vector gate to regulate the deviation under ranking supervision.
Rongfeng Guo, Yinxuan Huang, Wei Chen et al.· Proceedings of the 32nd ACM...· 0 citations
This work develops a multiagent simulation of a popular social network, Reddit, and uses millions of posts from users on the platform to model content-sharing on the platform.
Swapneel Mehta, Bogdan State, Richard Bonneau et al.· 1 citation
Trust and reputation systems underpin reliable interactions in large, distributed networks. However, conventional models typically propagate trust only forward, offering no accountability for endorsers regarding whom they vouch for, and leaving newly joined nodes without a meaningful initial reputation. RepuLink addresses these limitations by proposing a two-layer trust and reputation model that integrates direct interaction feedback with domain-specific endorsements. Crucially, it holds endorsers accountable via Backward Endorsement Penalty/Reward Propagation (BEPP/BERP). This paper demonstrates RepuLink-Tool, a deployable, full-stack reference implementation of this model. The application enables nodes to interact, rate, and endorse each other, while tracking reputation via a live dashboard and an interactive trust network graph. Furthermore, we introduce a new Linked Data layer built on top of the application. This layer features a lightweight OWL ontology encompassing nodes, interactions, ratings, endorsements, pairwise trust assessments, and computed reputation scores annotated with PROV-O provenance. It also provides an on-the-fly RDF projection of each user's trust network in multiple serialisations, alongside a scoped SPARQL endpoint that nodes can query live against their own data.
Wenbo Wu, George Konstantinidis· 0 citations
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