Finding node associations across different networks is the cornerstone behind a wealth of highimpact data mining applications. Traditional approaches are often, explicitly or implicitly, built upon the linearity and/or consistency assumptions. On the other hand, the recent network embedding based methods promise a natural way to handle the nonlinearity, yet they could suffer from the disparate node embedding space of different networks. In this paper, we address these limitations and tackle crossnetwork node associations from a new angle, i.e., crossnetwork transformation. We ask a generic question: Given two different networks, how can we transform one network to another? We propose an endtoend model that learns a composition of nonlinear operations so that one network can be transformed to another in a hierarchical manner. The proposed model bears three distinctive advantages. First (composite transformation), it goes beyond the linearity/consistency assumptions and performs the crossnetwork transformation through a composition of nonlinear computations. Second (representation power), it can learn the transformation of both network structures and node attributes at different resolutions while identifying the crossnetwork node associations. Third (generality), it can be applied to various tasks, including network alignment, recommendation, crosslayer dependency inference. Extensive experiments on different tasks validate and verify the effectivenessmore »
BRIGHT: A Bridging Algorithm for Network Alignment
Multiple networks emerge in a wealth of highimpact applications. Network alignment, which aims to find the node correspondence across different networks, plays a fundamental role for many data mining tasks. Most of the existing methods can be divided into two categories: (1) consistency optimization based methods, which often explicitly assume the alignment to be consistent in terms of neighborhood topology and attribute across networks, and (2) network embedding based methods which learn lowdimensional node embedding vectors to infer alignment. In this paper, by analyzing certain methods of these two categories, we show that (1) the consistency optimization based methods are essentially specific random walk propagations from anchor links that might be restrictive; (2) the embedding based methods no longer explicitly assume alignment consistency but inevitably suffer from the space disparity issue. To overcome these two limitations, we bridge these methods and propose a novel family of network alignment algorithms BRIGHT to handle both nonattributed and attributed networks. Specifically, it constructs a space by random walk with restart (RWR) whose bases are onehot encoding vectors of anchor nodes, followed by a shared linear layer. Our experiments on realworld networks show that the proposed family of algorithms BRIGHT outperform the stateofthe arts more »
 Publication Date:
 NSFPAR ID:
 10232522
 Journal Name:
 TheWebConf
 Sponsoring Org:
 National Science Foundation
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