Results 181 to 190 of about 3,653 (212)

Deep Attributed Network Embedding by Preserving Structure and Attribute Information

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021
Network embedding aims to learn distributed vector representations of nodes in a network. The problem of network embedding is fundamentally important. It plays crucial roles in many applications, such as node classification, link prediction, and so on. As the real-world networks are often sparse with few observed links, many recent works have utilized ...
Richang Hong, Le Wu, Yong Ge
exaly   +2 more sources

Attribute Network Alignment Based on Network Embedding

2021 7th International Conference on Computing and Data Engineering, 2021
Nodes with similar network structure and attribute features probably distribute across different networks. For instance, people tend to have accounts across various social networks. In recent years, network alignment to identify potential correspondences between nodes across networks has been research focus on social computing.
Fan Yang, Wenxin Liang, Linlin Zong
openaire   +1 more source

Neural Networks for Author Attribution

2007 IEEE International Fuzzy Systems Conference, 2007
The present article investigates the effectiveness of neural network models when applied to the task of categorising texts in the Greek language based on the style of their authors. Multilayer perceptrons (MLP), radial basis function networks (RBF) and self-organizing maps (SOM) are comparatively studied on the task of classifying documents based on a ...
Nikolaos Tsimboukakis, George Tambouratzis   +1 more
openaire   +1 more source

Attributed Signed Network Embedding

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
The major task of network embedding is to learn low-dimensional vector representations of social-network nodes. It facilitates many analytical tasks such as link prediction and node clustering and thus has attracted increasing attention. The majority of existing embedding algorithms are designed for unsigned social networks.
Suhang Wang, Charu C. Aggarwal, Jiliang Tang, Huan Liu 0001   +3 more
openaire   +1 more source

Finding attribute diversified community over large attributed networks

World Wide Web, 2021
Well connected users are generally discovered in communities which is one of the most important tasks for network data analytics and has tremendous real applications. In recent years, community search in attributed graphs has begun to attract attention, which aims to find communities that are both structure and attribute cohesive.
Afzal Azeem Chowdhary, Chengfei Liu, Lu Chen 0008, Rui Zhou 0001, Yun Yang 0001   +4 more
openaire   +2 more sources

Attributed Network Embedding with Community Preservation

2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020
Network embedding (NE) is a method that maps nodes in a network into a low-dimensional and continuous vector space while maintains inherent features of the network. Most existing algorithms for NE focus on one or two of the aspects of topological structure, node attributes or community structure information, but without integrating the three in a ...
Huang, T, Zhou, L, Wang, L, Du, G, Lü, K   +4 more
openaire   +1 more source

Attribute Normalization in Network Intrusion Detection

2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks, 2009
Anomaly intrusion detection is an important issue in computer network security. As a step of data preprocessing, attribute normalization is essential to detection performance. However, many anomaly detection methods do not normalize attributes before training and detection.
Wei Wang 0012, Xiangliang Zhang 0001, Sylvain Gombault, Svein J. Knapskog   +3 more
openaire   +2 more sources

Community Deception in Attributed Networks

IEEE Transactions on Computational Social Systems
Community detection algorithms that analyze networks to identify communities of nodes are an essential part of the network analysis toolkit used daily by different analysts (e.g., data scientists and law enforcement). However, there is not enough awareness that members of a community C (either revealed or not) inside a network G could act strategically
Fionda, Valeria, Pirrò, Giuseppe
openaire   +4 more sources

ANCA : Attributed Network Clustering Algorithm

2017
Graph clustering techniques are very useful for detecting densely connected groups in large graphs. Many existing graph clustering methods mainly focus on the topological structure, but ignore the vertex properties. Existing graph clustering methods have been recently extended to deal with nodes attribute.
Issam Falih, Nistor Grozavu, Rushed Kanawati, Younès Bennani   +3 more
openaire   +1 more source

Variational co-embedding learning for attributed network clustering

Knowledge-Based Systems, 2023
Shuiqiao Yang, Sunny Verma, Borui Cai
exaly