Stochastic Graphlet Embedding

Anjan Dutta, Hichem Sahbi

    Research output: Contribution to journalArticleResearch

    5 Citations (Scopus)


    © 2012 IEEE. Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semistructured data as graphs where nodes correspond to primitives (parts, interest points, and segments) and edges characterize the relationships between these primitives. However, these nonvectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of - explicit/implicit - graph vectorization and embedding. This embedding process should be resilient to intraclass graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have a positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
    Original languageEnglish
    Article number8587135
    Pages (from-to)2369-2382
    JournalIEEE Transactions on Neural Networks and Learning Systems
    Publication statusPublished - 1 Aug 2019


    • Betweenness centrality
    • graph classification
    • graph embedding
    • graph hashing
    • stochastic graphlets


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