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Heterogeneous Data Clustering Considering Multiple User-provided Constraints

Yue Huang
2019 International Journal of Computers Communications & Control  
Although numerous clustering methods have achieved remarkable success, current clustering methods for heterogeneous networks tend to consider only internal information of the dataset.  ...  Clustering on heterogeneous networks which consist of multi-typed objects and links has proved to be a useful technique in many scenarios.  ...  Huang In addition, relative to unsupervised clustering, semi-supervised clustering still has a small amount of useful sample information.  ... 
doi:10.15837/ijccc.2019.2.3419 fatcat:6mukj72i65fzpgjczkgm2l7rye

A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.  ...  There are two classes of community detection methods based on GCNs: (1) supervised/semi-supervised community classification, and (2) community clustering with unsupervised network representation.  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

Semi-supervised Clustering in Attributed Heterogeneous Information Networks

Xiang Li, Yao Wu, Martin Ester, Ben Kao, Xin Wang, Yudian Zheng
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects' relationships.  ...  We study the problem of clustering objects in an AHIN, taking into account objects' similarities with respect to both object attribute values and their structural connectedness in the network.  ...  In this paper we study the problem of semi-supervised clustering on attributed heterogeneous information networks.  ... 
doi:10.1145/3038912.3052576 dblp:conf/www/LiWEKWZ17 fatcat:jxswy4mi5rbtffosuznyoofri4

A Survey of Heterogeneous Information Network Analysis [article]

Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, Philip S. Yu
2015 arXiv   pre-print
We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research  ...  In this paper, we provide a survey of heterogeneous information network analysis.  ...  [15] present a semi-supervised clustering algorithm to generate different clustering results with path selection according to user guidance. Luo et al.  ... 
arXiv:1511.04854v1 fatcat:n2k3sulq3fbq3e34lrfrv3uoou

Author Name Disambiguation in Bibliographic Databases: A Survey [article]

Muhammad Shoaib, Ali Daud, Tehmina Amjad
2020 arXiv   pre-print
These steps are; (1) Preparation of dataset (2) Selection of publication attributes (3) Selection of similarity metrics (4) Selection of models and (5) Clustering Performance evaluation.  ...  Entity resolution is a challenging and hot research area in the field of Information Systems since last decade.  ...  Semi-Supervised Methods Semi-supervised Learning approaches [31] have also been applied to AND in BD. It combines the characteristics of both supervised and unsupervised methods. On et al.  ... 
arXiv:2004.06391v1 fatcat:g6ohfpzeejbwhlxmt7vlmyjqo4

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [article]

Di Jin, Cuiying Huo, Jianwu Dang, Peican Zhu, Weixiong Zhang, Witold Pedrycz, Lingfei Wu
2022 arXiv   pre-print
Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming.  ...  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs.  ...  The above methods are all based on semi-supervised, and recently some methods based on unsupervised have also been proposed.  ... 
arXiv:2205.00256v1 fatcat:w2e7lrsqcjgt3jz74a2ekvl2si

Mining heterogeneous information networks

Yizhou Sun, Jiawei Han
2013 SIGKDD Explorations  
and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks.  ...  Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks.  ...  For example, a co-author network can be obtained by projection on co-author information from a more complete heterogeneous bibliographic network.  ... 
doi:10.1145/2481244.2481248 fatcat:ovj4ra5qqjb6xjoox64ctdlfwm

A probabilistic framework for relational clustering

Bo Long, Zhongfei Mark Zhang, Philip S. Yu
2007 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07  
, semi-supervised clustering, co-clustering and graph clustering.  ...  , and semi-supervised clustering based on hidden Markov random fields.  ...  clustering, semi-supervised clustering, co-clustering and graph clustering.  ... 
doi:10.1145/1281192.1281244 dblp:conf/kdd/LongZY07 fatcat:q2sy5rjm5jbybai3talxp2v7nm

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks [article]

Jianxin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He
2021 arXiv   pre-print
Under the HAE framework, we propose a Higher-order Attribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network representation learning.  ...  Graph neural networks (GNNs) have been widely used in deep learning on graphs.  ...  Network representation learning methods can be divided into unsupervised and semi-supervised ones.  ... 
arXiv:2104.07892v1 fatcat:k2u2xipe3nfkvmnnet2mbzqczm

Mining Knowledge from Data: An Information Network Analysis Approach

Jiawei Han, Yizhou Sun, Xifeng Yan, Philip S. Yu
2012 2012 IEEE 28th International Conference on Data Engineering  
We show that heterogeneous information networks are informative, and link analysis on such networks is powerful at uncovering critical knowledge hidden in large semi-structured datasets.  ...  Departing from both, we view interconnected, semi-structured datasets as heterogeneous, information-rich networks and study how to uncover hidden knowledge in such networks.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
doi:10.1109/icde.2012.145 dblp:conf/icde/HanSYY12 fatcat:3yobocvu4venromba6dcshtw2e

Reliable Event Detection via Multiple Edge Computing on Streaming Traffic Social Data

Yipeng Ji, Jingyi Wang, Yan Niu, Hongyuan Ma
2021 IEEE Access  
Then, we utilize graph neural networks to perform semi-supervised learning on HIN to obtain the optimal meta-path weights.  ...  First, we combine trafficrelated knowledge information to extract various types of elements from social media texts, and accordingly construct a traffic event-based heterogeneous information network (HIN  ...  This constraint makes the heterogeneous information network semi-structured [31] . Meta-path is a very important concept in heterogeneous information networks.  ... 
doi:10.1109/access.2021.3060624 fatcat:5finlsbexjam7jw6cy4rkbb6zy

How to Better Identify Venture Capital Network Communities: Exploration of A Semi-Supervised Community Detection Method

Hong Xiong, Ying Fan
2021 Journal of Social Computing  
), as well as the support from the Tencent Research Institute Project "Research on Identification of Opinion Leaders Based on QQ Big Data" (No. 20182001706).  ...  Acknowledgment We are grateful for the financial support from the Chinese Natural Science Foundation Project "Social Network in Big Data Analysis: A Case in Investment Network" (Nos. 71372053 and 71731002  ...  We decided to use information about how industry leaders form clusters and how their circles realize a semi-supervised community detection for the VC network.  ... 
doi:10.23919/jsc.2020.0008 fatcat:2nugvlqrjzhldhheymdhnhxhke

HeMI: Multi-view Embedding in Heterogeneous Graphs [article]

Costas Mavromatis, George Karypis
2021 arXiv   pre-print
By extensive experiments on node classification, node clustering, and link prediction tasks, we show that the proposed self-supervision both outperforms and improves competing methods by 1% and up to 10%  ...  In this paper, we propose a self-supervised method that learns HG representations by relying on knowledge exchange and discovery among different HG structural semantics (meta-paths).  ...  Genre labels depend more on the plot of a movie (node attributes) and HeMI gives extra importance on the features by using fake attributes.  ... 
arXiv:2109.07008v1 fatcat:d3qseozemna3jicchpnjjmxx3u

Graph Neural Networks: Methods, Applications, and Opportunities [article]

Lilapati Waikhom, Ripon Patgiri
2021 arXiv   pre-print
This article provides a comprehensive survey of graph neural networks (GNNs) in each learning setting: supervised, unsupervised, semi-supervised, and self-supervised learning.  ...  Several semi-supervised survey articles [139] are focused on traditional ways of dealing with semi-supervised settings.  ...  [2] , and vertex representations in heterogeneous graphs as in Dong et al. [40] . Graph-Based Semi-Supervised Learning Semi-supervised learning has been around for many years.  ... 
arXiv:2108.10733v2 fatcat:j3rfmkiwenebvmfyboasjmx4nu

Semi-Supervised Deep Learning for Multiplex Networks [article]

Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran
2021 arXiv   pre-print
In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks.  ...  Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures  ...  Conclusion In this study, we propose a semi-supervised framework for representation learning in multiplex networks.  ... 
arXiv:2110.02038v1 fatcat:koof45ms6fbrpaz6izecoswroi
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