A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

Jin, Di; Yu, Zhizhi; Jiao, Pengfei; Pan, Shirui; He, Dongxiao; Wu, Jia; Yu, Philip S.; Zhang, Weixiong

doi:10.1109/tkde.2021.3104155

Cited by 162 publications

(45 citation statements)

References 140 publications

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“…Tasks on graphs can be categorised into node-focused tasks and graph-focused tasks [51]. Nodefocused tasks mainly include node classification, node ranking, link prediction, and community detection [79]. Graph-focused tasks mainly include graph classification, graph matching, and graph generation [51].…”

Section: Conceptsmentioning

confidence: 99%

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Zhang¹,

Wu²,

Yuan³

et al. 2022

Preprint

Self Cite

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Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only requirement for GNNs. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks, unexplainable discrimination against disadvantaged groups, or excessive resource consumption in edge computing environments. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness. To this end, we propose a comprehensive roadmap to build trustworthy GNNs from the view of the various computing technologies involved. In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. Additionally, we highlight the intricate cross-aspect relations between the above six aspects of trustworthy GNNs. Finally, we present a thorough overview of trending directions for facilitating the research and industrialisation of trustworthy GNNs.

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Section: Conceptsmentioning

confidence: 99%

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Zhang¹,

Wu²,

Yuan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…But due to the existence of overfitting (Nickel, Tresp, and Kriegel 2011), their performance lags behind the DB model. There are also some neural network models such as (Jin et al 2021b), (Yu et al 2021) (Jin et al 2021a) and(Nathani et al 2019), but they also have the risk of overfitting due to the huge number of parameters.…”

Section: Related Workmentioning

confidence: 99%

ER: Equivariance Regularizer for Knowledge Graph Completion

Cao

Yang

et al. 2022

AAAI

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Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the “size” of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. Our experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods.

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“…In the literature, graph neural networks focus on applying deep learning technologies to obtain dense feature representations for vertices in networks, which could be further used in downstream tasks such as node classification, link prediction, and subgraph classifications [11,40,45]. Some works generate the vertex sequences based on random walks then apply the skip2gram algorithm [22,23] to get vertex embeddings [6,25].…”

Section: Graph Neural Networkmentioning

confidence: 99%

Outside In

Hang

Dong

Zhao

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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As an emerging initiative of proactive human resource management, employee turnover prediction is critically important for employers to retain talents and avoid the loss of intellectual capital. While considerable research efforts have been made in this direction, most of them only focus on modeling the within-company career trajectories of employees where the influence of external job market has been largely neglected. To this end, in this paper, we propose an enhanced framework of employee turnover prediction by jointly modeling the turnover clues from both internal and external views. Specifically, from the external-market view, we construct a heterogeneous graph which connects the employees with external job markets through shared skills. In this way, we can capture the potential popularity of employees in external markets specific to skills. Meanwhile, from the internal-company view, we design a graph convolutional network with hierarchical attention mechanism to capture the influence of organizational structure (e.g., superiors, subordinates, and peers) and colleagues with similar skills. Furthermore, both modules are modeled with Bidirectional LSTM and survival analysis to learn effective and dynamic representations of employee turnover prediction. Finally, we conduct extensive experiments on a large-scale real-world talent dataset with state-ofthe-art methods, which clearly demonstrate the effectiveness of our approach as well as some interesting findings that could help us understand the employee turnover patterns, such as different impacts of external systems and collaborators from different groups.

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A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

Cited by 162 publications

References 140 publications

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

ER: Equivariance Regularizer for Knowledge Graph Completion

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