GCCAD: Graph Contrastive Learning for Anomaly Detection

Chen, Bo; Zhang, Jing; Zhang, Xiaokang; Dong, Yuxiao; Song, Jian; Zhang, Peng; Xu, Kui; Kharlamov, Evgeny; Tang, Jie

doi:10.1109/tkde.2022.3200459

Cited by 28 publications

(25 citation statements)

References 43 publications

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“…[17,37,46]. However, the assignment mistakes, as shown in Figure 2, hamper the development of effective algorithms [4,44]. Others attempt to manually label a small amount of data based on noisy data from existing databases to reduce data noises [11,13,20,24,26,30,32,34,38,47].…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the IND task is vital to guarantee the reliability of academic systems. Unfortunately, the issue has not received much attention [4].…”

Section: Related Workmentioning

confidence: 99%

“…In particular, even though several name disambiguation benchmarks, such as PubMed [40,45], MAG [46], DBLP [14], etc. [17,37], have been directly harvested from existing digital libraries, inevitably spurious information and assignment mistakes, as shown in Figure 2, are detrimental to build effective algorithms [4,44]. In light of this, others attempt to manually annotate a small amount of high-quality data from the online noisy data in order to reduce the negative impact of these noises [11,30,34].…”

Section: Introductionmentioning

confidence: 99%

“…To date, three-round contests have been held on the first two tasks, attracting more than 3,000 researchers. Furthermore, we host a regular leaderboard 4 to keep track of recent advances. The contest for the IND task is under active preparation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit

Chen¹,

Zhang²,

Zhang³

et al. 2023

Preprint

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Name disambiguation-a fundamental problem in online academic systems-is now facing greater challenges with the increasing growth of research papers. For example, on AMiner, an online academic search platform, about 10% of names own more than 100 authors. Such real-world hard cases cannot be fully addressed by existing research efforts, because of the small-scale or low-quality datasets that they use to build algorithms. The development of effective algorithms is further hampered by a variety of tasks and evaluation protocols designed on top of diverse datasets. To this end, we present WhoIsWho owning, a large-scale benchmark with over 1,000,000 papers built using an interactive annotation process, a regular leaderboard with comprehensive tasks, and an easy-touse toolkit encapsulating the entire pipeline as well as the most powerful features and baseline models for tackling the tasks. Our developed strong baseline has already been deployed online in the AMiner system to enable daily arXiv paper assignments. The documentation and regular leaderboards are publicly available at http://whoiswho.biendata.xyz/.

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Section: Related Workmentioning

confidence: 99%

“…Thus, the IND task is vital to guarantee the reliability of academic systems. Unfortunately, the issue has not received much attention [4].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit

Chen¹,

Zhang²,

Zhang³

et al. 2023

Preprint

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show abstract

“…[30][31][32][33][34][35] So far, RL has been developed and successfully applied to various types of data sets other than MD data. For image data, RL extracts informative representations for image classification, 36,37 anomaly detection, 38,39 and object detection. 40,41 For language data, RL provides low-dimensional embedding for sentiment analysis, 42,43 named entity recognition, 44,45 and language translation.…”

Section: Introductionmentioning

confidence: 99%

Representation of Protein Dynamics Disentangled by Time-structure-based Prior

Ishizone,

Matsunaga,

Fuchigami

et al. 2023

Preprint

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Representation learning (RL) is a universal technique for deriving low-dimensional disentangled representations from high-dimensional observations, aiding a multitude of downstream tasks. RL has been extensively applied to various data types, including images and natural language. Here, we analyze molecular dynamics (MD) simulation data of biomolecules in terms of RL to obtain disentangled representations related to their conformational transitions. Currently, state-of-the-art RL techniques, which are mainly motivated by the variational principle, try to capture slow motions in the representation (latent) space. Here, we propose two methods based on alternative perspective on the disentanglement in the representation space. The methods introduce a simple prior that imposes temporal constraints in the representation space, serving as a regularization term to facilitate capturing disentangled representations of dynamics. The introduction of this simple prior aids in characterizing the conformational transitions of proteins. Indeed, comparison with other methods via the analysis of MD simulation trajectories for alanine dipeptide and chignolin validates that the proposed methods construct Markov state models (MSMs) whose implied time scales are comparable to state-of-the-art methods. By coarse-graining MSMs, we further show the methods aid to detect physically important interactions for conformational transitions. Overall, our methods provide good representations of complex biomolecular dynamics for downstream tasks, allowing for better interpretations of conformational transitions.

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Discriminative Graph-Level Anomaly Detection via Dual-Students-Teacher Model

Lin,

Luo,

et al. 2023

Advanced Data Mining and Applications

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GCCAD: Graph Contrastive Learning for Anomaly Detection

Cited by 28 publications

References 43 publications

Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit

Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit

Representation of Protein Dynamics Disentangled by Time-structure-based Prior

Discriminative Graph-Level Anomaly Detection via Dual-Students-Teacher Model

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