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- research-article
Interdependent Causal Networks for Root Cause Localization
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 5051-5060• https://doi.org/10.1145/3580305.3599849The goal of root cause analysis is to identify the underlying causes of system problems by discovering and analyzing the causal structure from system monitoring data. It is indispensable for maintaining the stability and robustness of large-scale complex ...
- research-article
Time series contrastive learning with information-aware augmentations
- Dongsheng Luo,
- Wei Cheng,
- Yingheng Wang,
- Dongkuan Xu,
- Jingchao Ni,
- Wenchao Yu,
- Xuchao Zhang,
- Yanchi Liu,
- Yuncong Chen,
- Haifeng Chen,
- Xiang Zhang
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence•February 2023, Article No.: 506, pp 4534-4542• https://doi.org/10.1609/aaai.v37i4.25575Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive learning ...
- research-article
Towards Learning Disentangled Representations for Time Series
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 3270-3278• https://doi.org/10.1145/3534678.3539140Promising progress has been made toward learning efficient time series representations in recent years, but the learned representations often lack interpretability and do not encode semantic meanings by the complex interactions of many latent factors. ...
- research-article
Interpreting Convolutional Sequence Model by Learning Local Prototypes with Adaptation Regularization
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management•October 2021, pp 1366-1375• https://doi.org/10.1145/3459637.3482355In many high-stakes applications of machine learning models, outputting only predictions or providing statistical confidence is usually insufficient to gain trust from end users, who often prefer a transparent reasoning paradigm. Despite the recent ...
- research-article
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management•October 2021, pp 3747-3756• https://doi.org/10.1145/3459637.3481955Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Existing network embedding based methods have mostly focused on learning good node representations, whereas ...
- research-article
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection
MM '21: Proceedings of the 29th ACM International Conference on Multimedia•October 2021, pp 5546-5554• https://doi.org/10.1145/3474085.3475693Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable. Despite that many approaches have been developed to resolve this problem,...
- research-article
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining•August 2021, pp 3726-3734• https://doi.org/10.1145/3447548.3467125Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed increasing efforts in detecting anomalies with discrete event sequences. ...
- research-article
Learning to Drop: Robust Graph Neural Network via Topological Denoising
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining•March 2021, pp 779-787• https://doi.org/10.1145/3437963.3441734Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along the edges of the given graph. Despite their success, however, the existing GNNs are usually ...
- Article
Node Classification in Temporal Graphs Through Stochastic Sparsification and Temporal Structural Convolution
Machine Learning and Knowledge Discovery in Databases•September 2020, pp 330-346• https://doi.org/10.1007/978-3-030-67664-3_20AbstractNode classification in temporal graphs aims to predict node labels based on historical observations. In real-world applications, temporal graphs are complex with both graph topology and node attributes evolving rapidly, which poses a high ...
- research-article
Robust graph representation learning via neural sparsification
ICML'20: Proceedings of the 37th International Conference on Machine Learning•July 2020, Article No.: 1062, pp 11458-11468Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection. Reallife graphs usually have complex information in the local neighborhood, where each node is described by a rich set ...
- research-article
Deep Multi-Graph Clustering via Attentive Cross-Graph Association
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining•January 2020, pp 393-401• https://doi.org/10.1145/3336191.3371806Multi-graph clustering aims to improve clustering accuracy by leveraging information from different domains, which has been shown to be extremely effective for achieving better clustering results than single graph based clustering algorithms. Despite ...
- research-article
The multi-walker chain and its application in local community detection
Knowledge and Information Systems, Volume 60, Issue 3•Sep 2019, pp 1663-1691 • https://doi.org/10.1007/s10115-018-1247-1AbstractLocal community detection (or local clustering) is of fundamental importance in large network analysis. Random walk-based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. ...
- Article
Heterogeneous graph matching networks for unknown malware detection
- Shen Wang,
- Zhengzhang Chen,
- Xiao Yu,
- Ding Li,
- Jingchao Ni,
- Lu-An Tang,
- Jiaping Gui,
- Zhichun Li,
- Haifeng Chen,
- Philip S. Yu
IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence•August 2019, pp 3762-3770Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based ...
- research-article
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data
- Chuxu Zhang,
- Dongjin Song,
- Yuncong Chen,
- Xinyang Feng,
- Cristian Lumezanu,
- Wei Cheng,
- Jingchao Ni,
- Bo Zong,
- Haifeng Chen,
- Nitesh V. Chawla
AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence•January 2019, Article No.: 174, pp 1409-1416• https://doi.org/10.1609/aaai.v33i01.33011409Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multi-variate time series refer to identifying abnormal status in certain time ...
- research-article
Co-Regularized Deep Multi-Network Embedding
WWW '18: Proceedings of the 2018 World Wide Web Conference•April 2018, pp 469-478• https://doi.org/10.1145/3178876.3186113Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the ...
- research-article
Ranking Causal Anomalies for System Fault Diagnosis via Temporal and Dynamical Analysis on Vanishing Correlations
ACM Transactions on Knowledge Discovery from Data, Volume 11, Issue 4•November 2017, Article No.: 40, pp 1-28 • https://doi.org/10.1145/3046946Detecting system anomalies is an important problem in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be powerful in characterizing complex system behaviours. In the invariant network,...
- research-article
Flexible and Robust Multi-Network Clustering
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining•August 2015, pp 835-844• https://doi.org/10.1145/2783258.2783262Integrating multiple graphs (or networks) has been shown to be a promising approach to improve the graph clustering accuracy. Various multi-view and multi-domain graph clustering methods have recently been developed to integrate multiple networks. In ...
- research-article
Inside the atoms: ranking on a network of networks
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining•August 2014, pp 1356-1365• https://doi.org/10.1145/2623330.2623643Networks are prevalent and have posed many fascinating research questions. How can we spot similar users, e.g., virtual identical twins, in Cleveland for a New Yorker? Given a query disease, how can we prioritize its candidate genes by incorporating the ...