Multi-level social network alignment via adversarial learning and graphlet modeling

Aiming to identify corresponding users in different networks, social network alignment is significant for numerous subsequent applications. Most existing models apply consistency assumptions on undirected networks, ignoring platform disparity caused by diverse functionalities and universal directed relations like follower-followee. Due to indistinguishable nodes and relations, subgraph isomorphism is also unavoidable in neighborhoods. In order to precisely align directed and attributed social networks, we propose the Multi-level Adversarial and Graphlet-based Social Network Alignment (MAGSNA), which unifies networks as a whole at individual-level and learns discriminative graphlet-based features at partition-level simultaneously, thereby alleviating both platform disparity and subgraph isomorphism. Specifically, at individual-level, we relieve topology disparity by the random walk with restart, while developing directed weight-sharing network embeddings and a bidirectional optimizer on Wasserstein graph adversarial networks for attribute disparity. At partition-level, we extract overlapped partitions from graphlet orbits, then design weight-sharing partition embeddings and a hubness-aware refinement to derive discriminative features. By fusing the similarities of these two levels, we obtain a precise and thorough alignment. Experiments on real-world and synthetic datasets demonstrate that MAGSNA outperforms state-of-the-art methods, exhibiting competitive efficiency and superior robustness.