Social network alignment, identifying social accounts of the same individual across different social networks, shows fundamental importance in a wide spectrum of applications, such as link prediction and information diffusion. Individuals more often than not join in multiple social networks, and it is in fact much too expensive or even impossible to acquiring supervision for guiding the alignment. To the best of our knowledge, few method in the literature can align multiple social networks without supervision. In this article, we propose to study the problem of unsupervised multiple social network alignment. To address this problem, we propose a novel unsupervised model of joint Matrix factorization with a diagonal Cone under orthogonal Constraint, referred to as MC 2 . Its core idea is to embed and align multiple social networks in the common subspace via an unsupervised approach. Specifically, in MC 2 model, we first design a matrix optimization to infer the common subspace from different social networks. To address the nonconvex optimization, we then design an efficient alternating algorithm by leveraging its inherent functional property. Through extensive experiments on real-world datasets, we demonstrate that the proposed MC 2 model significantly outperforms the state-of-the-art methods.

Social network alignment, aligning different social networks on their common users, is receiving dramatic attentions from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA, unfolding the fruitful dynamics of social networks for user alignment. However, it faces challenges in both modeling and optimization: (1) To model the intra-network dynamics, we design a novel dynamic graph autoencoder to learn user embeddings with complex network dynamics. (2) To model the inter-network alignment, we design a unified optimization framework over proposed dynamic graph autoencoders, constructing a common subspace for user alignment across different networks. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed approach substantially outperforms the state-of-the-art methods.

Given a table of multi-dimensional data, what analyses would human create to extract information from it? From scientific exploration to business intelligence (BI), this is a key problem to solve towards automation of knowledge discovery and decision making. In this paper, we propose Table2Analysis to learn commonly conducted analysis patterns from large amount of (table, analysis) pairs, and recommend analyses for any given table even not seen before. Multi-dimensional data as input challenges existing model architectures and training techniques to fulfill the task. Based on deep Q-learning with heuristic search, Table2Analysis does table to sequence generation, with each sequence encoding an analysis. Table2Analysis has 0.78 recall at top-5 and 0.65 recall at top-1 in our evaluation against a large scale spreadsheet corpus on the PivotTable recommendation task.

Social network alignment, identifying social accounts of the same individual across different social networks, shows fundamental importance across a wide spectrum of applications. Individuals more often than not join in multiple social networks and it is in fact intractable or even impossible to acquiring supervision for guiding the alignment. However, to the best of our knowledge, none of existing methods can align multiple social networks without supervision. In this paper, we propose to study the problem of unsupervised multiple social network alignment. To address this problem, we propose a novel unsupervised model of Matrix factorization with diagonal Cone under orthogonal Constraint, referred to as MC2. Its core idea is to embed and align multiple social networks in the common subspace via an unsupervised approach. Specifically, in MC2model, we first design a matrix optimization to infer the common subspace from different social networks. To address the nonconvex optimization, we then design an efficient alternating algorithm by leveraging its inherent functional property. Through extensive experiments on real-world datasets, we demonstrate that the proposed MC2model significantly outperforms the state-of-the-art methods.

Social network alignment, aligning different social networks on their common users, is receiving dramatic attention from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic social Network Alignment (DNA) framework, a unified optimization approach over deep neural architectures, to unfold the fruitful dynamics to perform alignment. However, it faces tremendous challenges in both modeling and optimization: (1) To model the intra-network dynamics, we explore the local dynamics of the latent pattern in friending evolvement and the global consistency of the representation similarity with neighbors. We design a novel deep neural architecture to obtain the dual embedding capturing local dynamics and global consistency for each user. (2) To model the inter-network alignment, we exploit the underlying identity of an individual from the dual embedding in each dynamic social network. We design a unified optimization approach interplaying proposed deep neural architectures to construct a common subspace of identity embeddings. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed DNA framework substantially outperforms the state-of-the-art methods.