LINQS

STATISTICAL RELATIONAL LEARNING GROUP @ UMD



 

Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks

12th International SIGKDD Workshop on Mining and Learning with Graphs (MLG) - 2016
Download the publication : fakhraei_mlg_2016.pdf [728Ko]  
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 caring effects of different strategies in our proposed framework.

BibTex references

@InProceedings{fakhraei:mlg16,
  author       = "Fakhraei, Shobeir and Sridhar, Dhanya and Pujara, Jay and Getoor, Lise",
  title        = "Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks",
  booktitle    = "12th International SIGKDD Workshop on Mining and Learning with Graphs (MLG)",
  year         = "2016",
  organization = "ACM SIGKDD",
}

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