MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation

Su, Sen; Li, Sun; Zhang, Zhongbao; Li, Gen; Qu, Jielun

doi:10.24963/ijcai.2018/537

Cited by 23 publications

(14 citation statements)

References 15 publications

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“…With the development of network embedding, several embedding-based models show advantages on this task. Su et al [29] design a Constrained Dual Embedding model for multiple (more than two) social network, an effective NS-Alternation algorithm is adopted for the optimization. Zhang et al [31] propose a graph neural network model for alignment, in which the attribute embedding and structural embedding are incorporated into a convolutional neural network.…”

Section: Related Work a User Alignment Across Social Networkmentioning

confidence: 99%

ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

Liu

Zhang²,

et al. 2019

IEEE Access

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User alignment across social networks can facilitate more information/knowledge transferring across networks and thereby benefit several applications, including social link prediction, cross-domain recommendation, and information diffusion. Several works try to learn a common subspace for networks by preserving the structural proximities, such that different contribution weights of neighbors are ignored as users were always connected by unweighted edges. In this paper, we propose an attention-based network embedding model that exploits the social structures for user alignment. In particular, two main components are contained in our model framework: a masked graph attention mechanism which tries to learn the alignment task driven attention weights by the supervision of pre-aligned user pairs, and an embedding algorithm tries to learn a common vector space by explicitly modeling the weighted contribution probabilities between follower-ships and followee-ships. With the learned weights and embeddings transferring between these two components, we construct a unified model for user embedding and alignment. Stochastic gradient descent and negative sampling are adopted for efficient learning and scalability. The extensive experiments on real-world social network data sets demonstrate the effectiveness and robustness of the proposed model compared with several state-of-the-art methods. INDEX TERMS Attention mechanism, network embedding, social network analysis, user alignment.

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Section: Related Work a User Alignment Across Social Networkmentioning

confidence: 99%

ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

Liu

Zhang²,

et al. 2019

IEEE Access

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“…rule of Q t is of the same structure); 11 Update V s (or V t ) via Eqs. (14) and (15); 12 Generate candidate lists for alignment via computing the distance between the rows in V s and V t .…”

Section: A Updating Qmentioning

confidence: 99%

“…The right hand side in both Eq. (21) [4], [1], [11], [6], [9], [7], Twitter-Foursquare (TF), by collecting time information of friending. We restore the real-world dynamic social Table II.…”

Section: A Updating Qmentioning

confidence: 99%

“…• IONE [4]: It utilizes a unified optimization framework for aligning social networks subject to hard or soft constraint on the anchor users via an embedding approach. • MASTER [11]: It constructs a common subspace of multiple social networks via a semi-supervised optimization framework. Note that, it only considers structure information in this paper for fair comparison.…”

Section: A Updating Qmentioning

confidence: 99%

“…Some studies [2], [1], [25], [19], [26] incorporate heterogeneous information to discover the latent consistency of user identity, while others [3], [9], [4], [5], [6], [7], [27] exploit the structure information to perform social network alignment. Moreover, some studies [8], [10], [28], [11], [12], [13], [14], [15] investigate to exploit both structure and heterogeneous attribute information. However, all existing studies consider the social network to be static for simplicity and neglect its inherent dynamics.…”

Section: A Social Network Alignmentmentioning

confidence: 99%

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DNA: Dynamic Social Network Alignment

Zhang

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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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 intranetwork 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 realworld datasets and show that the proposed DNA framework substantially outperforms the state-of-the-art methods.

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Aligning Users Across Social Networks by Joint User and Label Consistence Representation

Gao

et al. 2019

Neural Information Processing

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MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation

Cited by 23 publications

References 15 publications

ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

DNA: Dynamic Social Network Alignment

Aligning Users Across Social Networks by Joint User and Label Consistence Representation

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