Wenchao Yu

Wenchao Yu

Ph.D. Candidate at University of California, Los Angeles

Publications

Modeling Co-Evolution Across Multiple Networks

Published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Multiple and co-evolving networks are common in many real settings such as social networks, communication networks and other information networks. Most of the work in the field of network evolution has focused on a single evolving network or specific network pairs, lacking generality in the analysis of multiple networks and ignoring the co-evolutionary dynamics between networks. In practice, a significant amount of information is encoded in the evolution of multiple networks with respect to one another. In this paper, we show how to use a \emph{shared temporal matrix factorization} framework to model co-evolution across multiple networks, and we refer to this framework as \textsc{CoEvol}. Specifically, the proposed framework decomposes the adjacency matrix of each co-evolving network into a product of network-independent shared factor and a set of network-specific temporal factors, and impose a non-negativity constraint on the factors for greater interpretability. Our approach has the potential to predict multiple changes in co-evolving networks over time, because of its ability to explicitly represent co-evolving networks as a function of time. The \textsc{CoEvol} framework also has the advantage of generality in addressing various temporal tasks across multiple networks. We show the benefits of this approach in predicting co-evolution across multiple networks on the tasks including cross-network link prediction, lag correlation detection and community detection. Compared to baseline methods, \textsc{CoEvol} obtains lower root mean-squared error in cross-network link prediction and higher cluster purity in community detection, which demonstrates that the \textsc{CoEvol} framework can capture the dynamics across multiple networks.

Recommended citation: Yu, Wenchao, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, and Wei Wang. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp. 2663-2671. ACM, 2018. https://epubs.siam.org/doi/abs/10.1137/1.9781611975321.76

Learning Deep Network Representations with Adversarially Regularized Autoencoders

Published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the ‘semantics’ of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.

Recommended citation: Yu, Wenchao, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, and Wei Wang. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp. 2663-2671. ACM, 2018. https://dl.acm.org/citation.cfm?id=3220000

NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks

Published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose “behaviors’ deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NetWalk has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3). flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NetWalk.

Recommended citation: Yu, Wenchao, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang. "NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2663-2671. ACM, 2018. https://dl.acm.org/citation.cfm?id=3220024