t-BNE: Tensor-based Brain Network Embedding

Cao, Binggang; He, Lifang; Wei, Xiaokai; Xing, Mengqi; Yu, Philip S.; Klumpp, Heide; Leow, Alex D.

doi:10.1137/1.9781611974973.22

Cited by 38 publications

(20 citation statements)

References 33 publications

(33 reference statements)

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“…Just like deep learning, tensor learning becomes very hot and popular topic in recent years due to the stronger computing capability and lower computation cost [5], [11], [12], [14], [19], [20], [24]. Coupled tensor matrix embedding tries to fuse multiple information sources where matrices and tensors sharing some common modes are jointly embedding [8].…”

Section: B Tensor Learning and Embeddingmentioning

confidence: 99%

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

Sun¹,

He²,

Huang³

et al. 2018

2018 IEEE International Conference on Big Knowledge (ICBK)

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Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information. However, current relevance computing based on meta-graph only considers the complex structural information, but ignores its embedded meta-paths information. To address this problem, we propose MEta-GrAph-based network embedding models, called MEGA and MEGA++, respectively. The MEGA model uses normalized relevance or similarity measures that are derived from a meta-graph and its embedded meta-paths between nodes simultaneously, and then leverages tensor decomposition method to perform node embedding. The MEGA++ further facilitates the use of coupled tensor-matrix decomposition method to obtain a joint embedding for nodes, which simultaneously considers the hidden relations of all meta information of a meta-graph. Extensive experiments on two real datasets demonstrate that MEGA and MEGA++ are more effective than state-of-the-art approaches.

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Section: B Tensor Learning and Embeddingmentioning

confidence: 99%

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

Sun¹,

He²,

Huang³

et al. 2018

2018 IEEE International Conference on Big Knowledge (ICBK)

Self Cite

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“…at. [2] propose a constrained tensor factorization algorithm; t-BNE, to learn brain network representations. This algorithm uses side information guidance to find an optimal set of features for brain graph classification.…”

Section: Related Workmentioning

confidence: 99%

t-PNE: Tensor-Based Predictable Node Embeddings

Al-Sayouri

Gujral

Koutra

et al. 2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream processes (e.g., node classification, link prediction), there is still room for improvement in different aspects, like efficacy, visualization, and interpretability. In this paper, we propose, t-PINE, a method that addresses these limitations. Contrary to baseline methods, which generally learn explicit graph representations by solely using an adjacency matrix, t-PINE avails a multi-view information graph-the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second viewin order to learn explicit and implicit node representations, using the Canonical Polyadic (a.k.a. CP) decomposition. We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful, highly predictable, and gracefully interpretable representations. Having good interpretable representations provides a good guidance to understand how each view contributes to the representation learning process. In addition, it helps us to exclude unrelated dimensions. Extensive experiments show that t-PINE drastically outperforms baseline methods by up to 158.6% with respect to Micro-F1, in several multi-label classification problems, while it has high visualization and interpretability utility.

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“…One major information that we want to preserve is the sequence of keystrokes. By using multi-view, we are able to maintain each view separately but then use multiple views to make predictions [5,17]. Recently, various methods have been proposed for this purpose [10,11,12].…”

Section: Data Processingmentioning

confidence: 99%

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

Sun

Wang

Cao

et al. 2017

Lecture Notes in Computer Science

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Abstract. With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.

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t-BNE: Tensor-based Brain Network Embedding

Cited by 38 publications

References 33 publications

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

t-PNE: Tensor-Based Predictable Node Embeddings

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

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