The Experts below are selected from a list of 55353 Experts worldwide ranked by ideXlab platform
Chengqi Zhang - One of the best experts on this subject based on the ideXlab platform.
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User Profile preserving social network embedding
International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
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IJCAI - User Profile preserving social network embedding
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
Daokun Zhang - One of the best experts on this subject based on the ideXlab platform.
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User Profile preserving social network embedding
International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
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IJCAI - User Profile preserving social network embedding
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
Jie Yin - One of the best experts on this subject based on the ideXlab platform.
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User Profile preserving social network embedding
International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
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IJCAI - User Profile preserving social network embedding
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
Xingquan Zhu - One of the best experts on this subject based on the ideXlab platform.
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User Profile preserving social network embedding
International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
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IJCAI - User Profile preserving social network embedding
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017Co-Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi ZhangAbstract:This paper addresses social network embedding, which aims to embed social network nodes, including User Profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore User-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, User Profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates User Profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed User Profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
Yanjun Wang - One of the best experts on this subject based on the ideXlab platform.
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Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks
IEEE Access, 2017Co-Authors: Yaqiong Qiao, Guangwu Hu, Yongzhong Huang, Meng Wang, Arun Kumar Sangaiah, Chaoqin Zhang, Yanjun WangAbstract:Along with the popularity of online social network (OSN), more and more OSN Users tend to create their accounts in different OSN platforms. Under such circumstances, identifying the same User among different OSNs offers tremendous opportunities for many applications, such as User identification, migration patterns, influence estimation, and expert finding in social media. Different from existing solutions which employ User Profile or social network structure alone, in this paper, we proposed a novel joint solution named MapMe, which takes both User Profile and social network structure feature into account, so that it can adapt more OSNs with more accurate results. MapMe first calculates User similarity via Profile features with the Doc2vec method. Then, it evaluates User similarity by analyzing User's ego network features. Finally, the Profile features and ego network features were combined to measure the similarity of the Users. Consequently, MapMe balances the two similarity factors to achieve goals in different platforms and scenarios. Finally, experiments are conducted on the synthetic and real data sets, proving that MapMe outperforms the existing methods with 10% on average.
