Jul 2026· IEEE International Conference on Engineering of Complex Computer Systems· pp. 1-6· 0 citations· 13 references
Abstract
Heterogeneous information networks serve as one of the most important platforms in the fields of big data and artificial intelligence. Node similarity analysis is a fundamental yet crucial task. To explicitly distinguish the number of differing items and the total magnitude of interaction information between nodes of the same type under arbitrary meta-paths, while enabling flexible weighting between these two aspects, this paper proposes a similarity algorithm that formulates node similarity as a convex combination of vector norms. The proposed algorithm requires only the interaction information between typed nodes and does not depend on the type of metapath. It satisfies desirable properties including non-negativity, symmetry, self-maximum and boundedness. Experimental results on two real-world datasets demonstrate its superiority: on the Association for Computing Machinery Digital Library Dataset, the proposed algorithm achieves an average Silhouette of 0.67 on the paper-author-paper meta-path, outperforming PathSim and HeteSim by over 139%, and maintains a Calinski-Harabasz index approximately 6.6 times that of PathSim on the optimal paper-subject-paper meta-path. On the Internet Movie Database Dataset, it attains an average Silhouette of 0.77, which is 5.1 times and 3.0 times those of PathSim and HeteSim, respectively. Furthermore, the algorithm exhibits strong robustness, with minimal performance fluctuation across different meta-paths and clustering algorithms, whereas baseline methods suffer severe degradation. These results validate the efficiency and robustness of the proposed algorithm under various complex scenarios.
This work proposes two scalable greedy algorithms based on gradient-guided edge selection that efficiently approximate marginal gains and prune the candidate edge set and achieves optimization performance comparable to exact greedy strategies while substantially reducing computational time.
Runze Zhang, Gengyu Wang, Zhongzhi Zhang· Proceedings of the 32nd ACM...· 0 citations
Real-world networks contain multiple entity and relation types, semantic paths, attribute nodes, and node features. Heterogeneous information networks (HINs) encode this heterogeneity, but placing all evidence in one graph space can obscure the distinct roles of relational structure and attributes and complicate controlled merge decisions. We propose the Hierarchical Heterogeneous Information Network (HHIN) as a data model that organizes typed entity relations in a main structure layer and descriptive evidence in a strong attribute layer. We develop controllable approximate reduction as one instantiation. Fixed point refinement grounded in behavioral equivalence yields stable structural candidates; two attribute Guards based on cosine similarity constrain merge admissibility; and a normalized semantic budget selects thresholds using similarity errors and normalized discounted cumulative gain at rank 10 (nDCG@10) computed from PathSim and HeteSim. Theory establishes termination, containment of accepted merges within stable candidate classes, and budget feasibility. On ACM, DBLP, and IMDB, the method reduces 7.22–21.78% of all nodes while retaining nDCG@10 values of at least 0.9735 for PathSim and 0.9910 for HeteSim. Across datasets, repeated retrieval and ranking yield speedups of 1.04–1.15×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} and 1.13–1.26×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}, respectively. On DBLP, paired tests with a relational graph convolutional network (R-GCN) and a heterogeneous graph attention network (HAN) do not detect significant classification differences between the original and reduced carriers, while mean epoch costs decrease by factors of 1.10 and 1.07. DBLP KMeans clustering and transductive label prediction from similarity neighborhoods provide additional downstream evaluations.
Qinggeng Jin, Wujie Hu, Yongjie Liang et al.· Journal of King Saud Univers...· 0 citations
This study introduces a new ranking framework that integrates a quasi-Laplacian structural measure with a gravity-inspired aggregation process and demonstrates that the proposed framework consistently outperforms existing techniques in terms of accuracy, resolution, and computational simplicity.
The results show that smaller clusters are generally more vulnerable to attacks on central nodes, whereas larger and less centralized clusters retain more topological efficiency.
Heterogeneous information networks (HINs) model typed entities and typed relations, where dense cross-type structures can reveal cohesive semantic patterns such as prolific author-paper-venue groups. Given a query meta-path, the densest P-partite subgraph search (DPpS) problem jointly selects a nonempty vertex set at each typed position and maximizes the number of induced meta-path instances normalized by the geometric mean of the selected set sizes. Existing exact methods solve DPpS by searching over iRM-sets and reducing each fixed-M problem to minimum-cut computations. However, their scalability is limited by the large number of candidate iRM-sets and the high cost of repeatedly solving large auxiliary networks. In this paper, we propose BoxDPpS, an efficient exact approach that reduces both sources of cost. It performs box-level search with safe region pruning, eliminates redundant representations of the same iRM-set, improves early pruning through bounded warm-up, and compresses each fixed-M auxiliary network for exact parametric pseudoflow solving. Experiments on seven real-world datasets show that BoxDPpS preserves the exact DPpS optimum while achieving an average speedup of 27.04x over the state-of-the-art method.
Jiadong Xie, Jiaming Yang, Kangfei Zhao et al.· 0 citations
This work generalizes three distance-based topological measures, namely closeness centrality, betweenness centrality and node eccentricity, using this new hypergraph distance, and shows that hypergraphs can be divided into three distinct classes, corresponding to the possible dominance of specific orders of interaction over their general metric structure.
E. Vasilyeva, L. Tupikina, D. Musatov et al.· Chaos, Solitons & Fracta...· 0 citations
