Bridging Number and Magnitude in Heterogeneous Information Networks Similarity: A Convex Combination Approach over Arbitrary Meta-Paths

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.