Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
This work addresses a domain-specific problem for researchers in graph-based machine learning, offering an incremental improvement over existing sequential graph construction methods.
The paper tackles the problem of constructing neighborhood graphs for inference in multi-relational networks by proposing a framework that dynamically adapts the graph based on intermediate inference results and structural properties, with preliminary results illustrating the effects of different strategies.
A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction. Most methods employ a sequential process to construct the neighborhood graph. This process often consists of generating a candidate graph, pruning the candidate graph to make a neighborhood graph, and then performing inference on the variables (i.e., nodes) in the neighborhood graph. In this paper, we propose a framework that can dynamically adapt the neighborhood graph based on the states of variables from intermediate inference results, as well as structural properties of the relations connecting them. A key strength of our framework is its ability to handle multi-relational data and employ varying amounts of relations for each instance based on the intermediate inference results. We formulate the link prediction task as inference on neighborhood graphs, and include preliminary results illustrating the effects of different strategies in our proposed framework.