Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process
Huafeng Liu, Tong Zhou, Jiaqi Wang
[AAAI-22] Main Track
Abstract:
In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e.g., graph, network). It provides an efficient neural relational model architecture by grouping nodes in a group-wise view rather than node-wise or edge-wise view. DaRM simultaneously learns what makes a group, how to divide nodes into groups, and how to adaptively control the number of groups. The dedicated group generative process is able to sufficiently exploit pair-wise or higher-order interactions between data points in both inter-group and intra-group, which is useful to sufficiently mine the hidden structure among data. A series of experiments have been conducted on both synthetic and real-world datasets. The experimental results demonstrated that DaRM can obtain high performance on both community detection and link prediction tasks.
Introduction Video
Sessions where this paper appears
-
Poster Session 1
Thu, February 24 4:45 PM - 6:30 PM (+00:00)
Blue 2
-
Poster Session 11
Mon, February 28 12:45 AM - 2:30 AM (+00:00)
Blue 2