Research of homogeneous ( i.e., single node and edge type) biological networks (BNs) has received significant attention. Graphlets have been proven in homogeneousBN research.Given their popularity, graphlets were extended to their directed or dynamic counterparts, owing to increase in availability of directed or temporal BNs. Given the increasing amounts of available BN data of different types, we generalize current homogeneous graphlets to their heterogeneous counterparts, which encompass different node or edge types and thus allow for analyzing a heterogeneous "network of networks". Heterogeneous graphlets will have at least as high impact as homogeneous graphlets have had. We illustrate the usefulness of heterogeneous graphlets in the context of network alignment (NA). While existing NA methods are homogeneous (they can only account for a single node type and a single edge type) , we generalize  a state-of-the-art homogeneous graphlet-based NAmethod into its heterogeneous counterpart. Because graphlets are applicable to many other network science problems, we provide an implementation of our heterogeneous graphlet counting approach (available upon request) .
Network alignment (NA) aims to find a node mapping between compared networks in order to uncover highly similar ( i.e., conserved) network regions. Existing NA methods are homogeneous, i.e., they can deal only with networks containing nodes and edges of one type. However, due to increasing amounts of heterogeneous real-world network data with nodes and edges of different types, we extend three recent state-of-the-art homogeneous NA methods, WAVE, MAGNA++, and SANA, to allow for heterogeneous NA for the first time. We introduce several algorithmic novelties to achieve this. Namely, these existing NA methods compute homogeneous graphlet-based node similarities and then find high-scoring alignments with respect to these similarities, while simultaneously maximizing the number of conserved edges. Instead, we generalize homogeneous graphlets to their heterogeneous counterparts, which we then use to develop a new measure of heterogeneous node similarity. Also , we generalize S^3, a state-of-the-art measure of edge conservation for homogeneous NA, to its heterogeneous counterpart. Then, we find high-scoring alignments with respect to our heterogeneous node similarity and edge conservation measures. In evaluations on synthetic networks and real-world biological network data, our proposed heterogeneous NA methods lead to higher-quality alignments and better robustness to noise in the data than their homogeneous counterparts .
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Research of homogeneous (
Network alignment (NA) aims to find a node mapping between compared networks in order to uncover highly similar (
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single node and edge type) biological networks (BNs) has received significant attention. Graphlets have been proven in homogeneousBN research.Given their popularity, graphlets were extended to their directed or dynamic counterparts, owing to increase in availability of directed or temporal BNs. Given the
conserved) network regions. Existing NA methods are homogeneous, i.e., they can deal only with networks containing nodes and edges of one type. However, due to
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available BN data
heterogeneous real-world network data with nodes and edges
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generalize current homogeneous graphlets to their heterogeneous counterparts, which encompass different node or edge types and thus allow for analyzing a heterogeneous "network of networks". Heterogeneous graphlets will have at least as high impact as homogeneous graphlets have had. We illustrate the usefulness of heterogeneous graphlets in the context of network alignment (NA). While
extend three recent state-of-the-art homogeneous NA methods, WAVE, MAGNA++, and SANA, to allow for heterogeneous NA for the first time. We introduce several algorithmic novelties to achieve this. Namely, these
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are homogeneous (they can only account for a single node type and a single edge type)
compute homogeneous graphlet-based node similarities and then find high-scoring alignments with respect to these similarities, while simultaneously maximizing the number of conserved edges. Instead, we generalize homogeneous graphlets to their heterogeneous counterparts, which we then use to develop a new measure of heterogeneous node similarity. Also
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S^3,
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homogeneous graphlet-based NAmethod into
measure of edge conservation for homogeneous NA, to
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Because graphlets are applicable to many other network science problems, we provide an implementation of our heterogeneous graphlet counting approach (available upon request)
Then, we find high-scoring alignments with respect to our heterogeneous node similarity and edge conservation measures. In evaluations on synthetic networks and real-world biological network data, our proposed heterogeneous NA methods lead to higher-quality alignments and better robustness to noise in the data than their homogeneous counterparts