Multimodal Network Alignment

A multimodal network encodes relationships between the same set of nodes in multiple settings, and network alignment is a powerful tool for transferring information and insight between a pair of networks. We propose a method for multimodal network alignment that computes a matrix which indicates the alignment, but produces the result as a low-rank factorization directly. We then propose new methods to compute approximate maximum weight matchings of low-rank matrices to produce an alignment. We evaluate our approach by applying it on synthetic networks and use it to de-anonymize a multimodal transportation network.Comment: 14 pages, 6 figures, Siam Data Mining 201

Spectral Alignment of Networks

Network alignment refers to the problem of finding a bijective mapping across vertices of two or more graphs to maximize the number of overlapping edges and/or to minimize the number of mismatched interactions across networks. This paper introduces a network alignment algorithm inspired by eigenvector analysis which creates a simple relaxation for the underlying quadratic assignment problem. Our method relaxes binary assignment constraints along the leading eigenvector of an alignment matrix which captures the structure of matched and mismatched interactions across networks. Our proposed algorithm denoted by EigeAlign has two steps. First, it computes the Perron-Frobenius eigenvector of the alignment matrix. Second, it uses this eigenvector in a linear optimization framework of maximum weight bipartite matching to infer bijective mappings across vertices of two graphs. Unlike existing network alignment methods, EigenAlign considers both matched and mismatched interactions in its optimization and therefore, it is effective in aligning networks even with low similarity. We show that, when certain technical conditions hold, the relaxation given by EigenAlign is asymptotically exact over Erdos-Renyi graphs with high probability. Moreover, for modular network structures, we show that EigenAlign can be used to split the large quadratic assignment optimization into small subproblems, enabling the use of computationally expensive, but tight semidefinite relaxations over each subproblem. Through simulations, we show the effectiveness of the EigenAlign algorithm in aligning various network structures including Erdos-Renyi, power law, and stochastic block models, under different noise models. Finally, we apply EigenAlign to compare gene regulatory networks across human, fly and worm species which we infer by integrating genome-wide functional and physical genomics datasets from ENCODE and modENCODE consortia. EigenAlign infers conserved regulatory interactions across these species despite large evolutionary distances spanned. We find strong conservation of centrally-connected genes and some biological pathways, especially for human-fly comparisons