Data

  • Career network data for PhDs in computer science
    Description: This dataset comprises two anonymized networks from our study on post-PhD careers in computing. The first is a weighted, directed, temporal network that represents transitions between employers. The second is a bipartite graph connecting employees and employers.
    Reference: Career Transitions and Trajectories: A Case Study in Computing. Tara Safavi, Maryam Davoodi, Danai Koutra. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), August 2018 [PDF]

Code

  • REGAL code
    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]

  • HashAlign code
    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]

  • FlowR code
    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]

  • EAGLE code
    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]

  • ABC-LSH code
    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]

  • Perseus-Hub code
    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]

Demos

  • ConDeNSe demo
    Description: We propose ConDeNSe (Conditional Diversified Network Summarization), a Minimum Description Length-based method that summarizes a given graph with approximate ‘supergraphs’ conditioned on diverse, predefined structural patterns.
    Reference: Reducing large graphs to small supergraphs: a unified approach. Yike Liu, Tara Safavi, Neil Shah, Danai Koutra. In Social Network Analysis and Mining (SNAM), February 2018 [PDF]