Stochastic Graphlet Embedding
Anjan Dutta
author
Hichem Sahbi
author
2018
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial 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 intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) 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 positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
Stochastic graphlets
Graph embedding
Graph classification
Graph hashing
Betweenness centrality
DAG; 602.167; 602.168; 600.097; 600.121
exported from refbase (http://refbase.cvc.uab.es/show.php?record=3225), last updated on Tue, 02 Jul 2019 13:50:12 +0200
text
http://refbase.cvc.uab.es/files/DuS2018.pdf
10.1109/TNNLS.2018.2884700
Admin @ si @ DuS2018
IEEE Transactions on Neural Networks and Learning Systems
TNNLS
2018
continuing
periodical
academic journal
1
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