Spectral K-way ratio-cut partitioning and clustering
DAC '93 Proceedings of the 30th international Design Automation Conference
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The budgeted maximum coverage problem
Information Processing Letters
A fast kernel-based multilevel algorithm for graph clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
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The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (ppi) using graph theoretic analysis. Despite the recent progress, systems level analysis of ppis remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize ppis at multiple resolutions to provide high level views of its functional landscape are still lacking. In this paper, we present a novel data-driven and generic algorithm called fuse (Functional Summary Generator) that generates functional maps of a ppi at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions. By simultaneously evaluating interaction and annotation data, fuse abstracts higher-order interaction maps by reducing the details of the underlying ppi to form a functional summary graph of interconnected functional clusters. To this end, fuse exploits Minimum Description Length (mdl) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Extensive experiments on real-world ppis demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with go term enrichment.