Description: We develop a scalable algorithm that uses learned structural node embeddings to match nodes across multiple graphs.
Reference: REGAL: Representation Learning-based Graph Alignment. Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra. In ACM Conference on Information and Knowledge Management (CIKM), October 2018 [PDF]
Description: We propose a locality-sensitive hashing (LSH) framework for matching nodes across multiple undirected, weighted, and/or attributed graphs.
Reference: HashAlign: Hash-based Alignment of Multiple Graphs. Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen, Danai Koutra. In Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), April 2018 [PDF]
Description: We introduce a two-step divide-and-conquer approach to solving linear systems in settings where many queries need to be handled. Our parallelizable method, FlowR, uses a one-time message exchange between subproblems. We further speed up our proposed method by extending our formulation to carefully designed overlapping subproblems (FlowR-OV) and by leveraging the strengths of iterative methods (FlowR-Hyb).
Reference: Fast Flow-based Random Walk with Restart in a Multi-query Setting. Yujun Yan, Mark Heimann, Di Jin, Danai Koutra. In Proceedings of the 2018 SIAM International Conference on Data Mining (SDM), February 2018 [PDF]
Description: We introduce EAGLE (Exploratory Analysis of Graphs with domain knowLEdge), a novel method that creates interpretable, feature-based, and domain-specific graph summaries in a fully automatic way.
Reference: Exploratory Analysis of Graph Data by Leveraging Domain Knowledge. Di Jin, Danai Koutra. In IEEE International Conference on Data Mining (ICDM), November 2017 [PDF]
Description: We propose a fast network discovery approach from time series based on ABC, a new locality-sensitive hashing (LSH) family, which randomly selects and matches time series subsequences.
Reference: Scalable Hashing-based Network Discovery. Tara Safavi, Chandra Sripada, Danai Koutra. In IEEE International Conference on Data Mining (ICDM), November 2017 [PDF]
Description: Perseus-Hub is an interactive graph summarization and anomaly detection system designed to help practitioners understand their data.
Reference: PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs. Di Jin, Aristotelis Leventidis, Haoming Shen, Ruowang Zhang, Junyue Wu, Danai Koutra. In Informatics, June 2017 [PDF]
VoG: Vocabulary-based summarization of Graphs code
Description: We propose VoG, which summarizes a graph via the subgraphs that describe it best. To do so, we use the Minimum Description Length (MDL) principle: a subgraph is included in the summary if it decreases the total description length of the graph.
Reference: VOG: Summarizing and Understanding Large Graphs. Danai Koutra, U Kang, Jilles Vreeken, Christos Faloutsos. In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), April 2014 [PDF]