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2020 Index IEEE Transactions on Knowledge and Data Engineering Vol. 32

2021 IEEE Transactions on Knowledge and Data Engineering  
Chen, Z., +, TKDE July 2020 1378-1392 Learning Distilled Graph for Large-Scale Social Network Data Clustering.  ...  ., +, TKDE Aug. 2020 1595-1609 Scalable Spectral Clustering for Overlapping Community Detection in Large-Scale Networks.  ...  Social sciences computing Discerning  ... 
doi:10.1109/tkde.2020.3038549 fatcat:75f5fmdrpjcwrasjylewyivtmu

InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance [article]

Cen Chen, Chen Liang, Jianbin Lin, Li Wang, Ziqi Liu, Xinxing Yang, Xiukun Wang, Jun Zhou, Yang Shuang, Yuan Qi
2020 arXiv   pre-print
used graphs, standard data processing procedures, and a uniform graph learning platform.  ...  In order to uncover the relations behind organized fraudsters and detect fraudulent claims, we developed a large-scale insurance fraud detection system, i.e., InfDetect, which provides interfaces for commonly  ...  with social networking features.  ... 
arXiv:2003.02833v3 fatcat:3ov6jodf2zekjck2f54lanko6m

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction [article]

Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, Katarzyna Musial
2020 arXiv   pre-print
Extensive experiments have been conducted on two large-scale real-life social networks.  ...  Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning  ...  To adapt MGCN for large scale social networks, and improve its scalability and efficiency, we propose a novel training method that first partitions the large-scale social networks into clusters and learns  ... 
arXiv:2006.01963v1 fatcat:6iql2ud4kndplgfdkqq53mawri

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs [article]

Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
2020 arXiv   pre-print
We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.  ...  We show Karate Club's efficiency with respect to learning performance on a wide range of real world clustering problems, classification tasks and support evidence with regards to its competitive speed.  ...  ACKNOWLEDGEMENTS Benedek Rozemberczki was supported by the Centre for Doctoral Training in Data Science, funded by EPSRC (grant EP/L016427/1).  ... 
arXiv:2003.04819v3 fatcat:wtjwypkjz5a77hw4b2kyi32hzq

Karate Club

Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
Graphs encode important structural properties of complex systems. Machine learning on graphs has therefore emerged as an important technique in research and applications.  ...  We show Karate Club's efficiency in learning performance on a wide range of real world clustering problems and classification tasks along with supporting evidence of its competitive speed.  ...  ACKNOWLEDGEMENTS Benedek Rozemberczki was supported by the Centre for Doctoral Training in Data Science, funded by EPSRC (grant EP/L016427/1).  ... 
doi:10.1145/3340531.3412757 dblp:conf/cikm/RozemberczkiKS20 fatcat:bplqx2zgknfjtgbzuj3743ruum

Novel Machine Learning Algorithms for Centrality and Cliques Detection in Youtube Social Networks

Craigory Coppola, Heba Elgazzar
2020 International Journal of Artificial Intelligence & Applications  
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target  ...  Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes.  ...  Such large-scale datasets allow for algorithms from graph theory and machine learning to take hold and become extremely useful [1] .  ... 
doi:10.5121/ijaia.2020.11106 fatcat:hgmsowogf5ardfpcrofr34urhu

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

Hongxu Chen, Hongzhi YIN, Xiangguo Sun, Tong Chen, Bogdan Gabrys, Katarzyna Musial
2020 Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining  
Extensive experiments have been conducted on two large-scale real-life social networks.  ...  Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning  ...  To adapt MGCN for large scale social networks, and improve its scalability and efficiency, we propose a novel training method that first partitions the large-scale social networks into clusters and learns  ... 
doi:10.1145/3394486.3403201 fatcat:jx7gb6a4vnfo5d6e2kkuc3m5cq

Transferring Knowledge Distillation for Multilingual Social Event Detection [article]

Jiaqian Ren and Hao Peng and Lei Jiang and Jia Wu and Yongxin Tong and Lihong Wang and Xu Bai and Bo Wang and Qiang Yang
2021 arXiv   pre-print
Recently published graph neural networks (GNNs) show promising performance at social event detection tasks.  ...  to make up for the paucity of annotated data.  ...  In our experiments, the network intended for French or Arabic event detection is regarded as the student and the network pre-trained on a large-scale English dataset is the teacher.  ... 
arXiv:2108.03084v2 fatcat:kiq6t3w7qnfxxa4bvitt6gdpse

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions [article]

Bilal Abu-Salih, Bushra Bremie, Pornpit Wongthongtham, Kevin Duan, Tomayess Issa, Kit Yan Chan, Mohammad Alhabashneh, Teshreen Albtoush, Sulaiman Alqahtani, Abdullah Alqahtani, Muteeb Alahmari, Naser Alshareef, Abdulaziz Albahlal
2019 arXiv   pre-print
The wealth of Social Big Data (SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues.  ...  Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application  ...  enhances the decision-making process and provides valuable insights from large-scale data.  ... 
arXiv:1902.10402v1 fatcat:rkbggcretncm5ngydhxkpf4o5q

Current and Future Challenges in Mining Large Networks

Lawrence B. Holder, Tina Eliassi-Rad, Aditya Prakash, Rajmonda Caceres, David F. Gleich, Jason Riedy, Maleq Khan, Nitesh V. Chawla, Ravi Kumar, Yinghui Wu, Christine Klymko
2016 SIGKDD Explorations  
We report on the Second Workshop on Mining Networks and Graphs held at the 2015 SIAM International Conference on Data Mining.  ...  This half-day workshop consisted of a keynote talk, four technical paper presentations, one demonstration, and a panel on future challenges in mining large networks.  ...  Some efficient methods for detecting communities in large-scale networks are presented in [38] and [10] .  ... 
doi:10.1145/2980765.2980770 fatcat:e7g5ju6icnclpmc6z56rprwqu4

LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning [article]

Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
2022 arXiv   pre-print
Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning.  ...  Specifically, for each node to be removed, its local information is obtained by subtracting the global information aggregated from its neighboring preserved nodes.  ...  Border Impact As a large number of data can be represented as graphs, e.g, social networks, protein networks and chemical networks.  ... 
arXiv:2204.12881v1 fatcat:24m6mn6j7ndj7kj7ey3653yszy

Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness [article]

Faxi Yuan, Chao Fan, Hamed Farahmand, Natalie Coleman, Amir Esmalian, Cheng-Chun Lee, Flavia I. Patrascu, Cheng Zhang, Shangjia Dong, Ali Mostafavi
2021 arXiv   pre-print
Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts.  ...  Third, we illustrate the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networks and the disrupted access to critical facilities, such as hospitals  ...  The authors would also like to acknowledge INRIX, Inc. and SafeGraph for providing data.  ... 
arXiv:2111.06461v2 fatcat:2ugdb6geivdsjp5ypzkspvldey

Data-Mining Research in Education [article]

Jiechao Cheng
2017 arXiv   pre-print
It focuses on analyzing educational related data to develop models for improving learners' learning experiences and enhancing institutional effectiveness.  ...  Applying data mining in education also known as educational data mining (EDM), which enables to better understand how students learn and identify how improve educational outcomes.  ...  As for small-scale, they can opt for clustering approach since it does not require necessary splitting data in classification. V.  ... 
arXiv:1703.10117v2 fatcat:4aujugkxcnbhlivfg5zt7of56e

CCGL: Contrastive Cascade Graph Learning [article]

Xovee Xu, Fan Zhou, Kunpeng Zhang, Siyuan Liu
2021 arXiv   pre-print
Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data.  ...  Semi-supervised learning facilitates unlabeled data for cascade understanding in pre-training.  ...  Datasets We used five large-scale publicly available cascade datasets, which can be categorized into two types: social and scientific.  ... 
arXiv:2107.12576v1 fatcat:kb3si37j65gntar64i63aq7hzi

Exploiting Social Networks for Large-Scale Human Behavior Modeling

Nicholas D. Lane, Ye Xu, Hong Lu, Andrew T. Campbell, Tanzeem Choudhury, Shane B. Eisenman
2011 IEEE pervasive computing  
Different forms of social network information are distilled into a weighted similarity graph between users.  ...  Comparing data and social network similarity. We begin with a simple experiment Large-ScaLe OppOrtuniStic SenSing to test the intuition that underpins the CoCo framework.  ...  Selected CS articles and columns are also available for free at http://ComputingNow.computer.org. Nicholas D. Lane is a researcher in the  ... 
doi:10.1109/mprv.2011.70 fatcat:mn3opllbxrhshjq4qtt6hf6hg4
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