Beyond Individuals: Modeling Mutual and Multiple Interactions for Inductive Link Prediction between Groups

计算机科学 联营 成对比较 链接(几何体) 人工智能 机器学习 群(周期表) 任务(项目管理) 图形 理论计算机科学 计算机网络 化学 管理 有机化学 经济
作者
Gongzhu Yin,Xing Wang,Hongli Zhang,Chao Meng,Yuchen Yang,Kun Lu,Yi Luo
标识
DOI:10.1145/3539597.3570448
摘要

Link prediction is a core task in graph machine learning with wide applications. However, little attention has been paid to link prediction between two group entities. This limits the application of the current approaches to many real-life problems, such as predicting collaborations between academic groups or recommending bundles of items to group users. Moreover, groups are often ephemeral or emergent, forcing the predicting model to deal with challenging inductive scenes. To fill this gap, we develop a framework composed of a GNN-based encoder and neural-based aggregating networks, namely the Mutual Multi-view Attention Networks (MMAN). First, we adopt GNN-based encoders to model multiple interactions among members and groups through propagating. Then, we develop MMAN to aggregate members' node representations into multi-view group representations and compute the final results by pooling pairwise scores between views. Specifically, several view-guided attention modules are adopted when learning multi-view group representations, thus capturing diversified member weights and multifaceted group characteristics. In this way, MMAN can further mimic the mutual and multiple interactions between groups. We conduct experiments on three datasets, including two academic group link prediction datasets and one bundle-to-group recommendation dataset. The results demonstrate that the proposed approach can achieve superior performance on both tasks compared with plain GNN-based methods and other aggregating methods.

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