This paper proposes SeSyCo, a Semantic-Symbolic Knowledge Consensus framework, which leverages the semantic space to diverge monolingual queries into broad multilingual evidence, and subsequently utilize the symbolic space to eliminate language discrepancies, converging the gathered information into a robust consensus...
Yu Zhang, Ran Song, Xiaofei Gao et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes Hyperspherical Representation Equilibrium Shift (HyRES), a theoretically grounded, unsupervised framework that redefines node influence as a geometric displacement within the latent space, and leverages Linear Response Theory to derive a closed-form approximation of the global representation shift ca...
Yan-Tuan Xian, Chun-Ping Li, Hong-Bin Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Identifying influential nodes in graph-structured data is a fundamental challenge. Traditional metrics ignore non-linear GNN semantics, while deep influence maximization methods require computationally expensive, simulation-based supervision. To bridge the gap between topological analysis and deep representation learni...
Yantuan Xian, Chunping Li, Hongbin Wang et al.· Proceedings of the 32nd ACM...· 0 citations
This paper proposes SeSyCo, a Semantic-Symbolic Knowledge Consensus framework, which leverages the semantic space to diverge monolingual queries into broad multilingual evidence, and subsequently utilize the symbolic space to eliminate language discrepancies, converging the gathered information into a robust consensus...
Yu Zhang, Ran Song, Xiaofei Gao et al.· Proceedings of the 32nd ACM...· 0 citations
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