MEDEN: the method presented in "Mining Heavy Subgraphs in Time-Evolving Networks" from ICDM'11 is available in source code and executable .
NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks. Code and executable for the method that appeared in SDM 2013
Quantifying Spatial Relationships from Whole Retinal Images. Matlab and EMD code for method that appeared in Bioinformatics 2013
Answering top-k queries over a mixture of attractive and repulsive dimensions. The tool and dataset for the work published in PVLDB 2011
TopGC: A tool for the scalable discovery of the best clusters in a graph. TopGC probabilistically finds the best clusters in large, edge weighted directed and undirected graphs, using a modification of Locality Sensitive Hashing (LSH). This work was published in VLDB, 2010.
GraphSig - A significant subgraph mining tool: The tool mines statistically significant subgraphs from large graph databases. The statistical significance of a graph is quantified by measuring its p-value. The work has been published in ICDE'2009. The tool has been extended to classify graphs and finds application in analyzing chemical libraries. Significant subgraphs can be employed to construct chemical descriptors and can then be classified in the feature space. The work on classification has been published in ACS Journal of Chemical Information and Modeling.
RRW: A graph clustering library based on repeated random walks. RRW is a tool that will cluster the nodes in edge weighted undirected graphs (such as gene or protein functional networks). Clusters are found by looking at the random walk distances between nodes. This work was published in BMC Bioinformatics.
Closure-tree: A subgraph indexing tool
SCT algorithm and an implementation of MCMC Bayesian network structure learning
Deriving phylogenetic tress from the similarity analysis of metabolic pathways