Results 251 to 260 of about 170,883 (277)

Node embedding for network community discovery

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
Neural node embedding has been recently developed as a powerful representation for supervised tasks with graph data. We leverage this recent advance and propose a novel approach for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate consistent improvement of the proposed ...
Christy Lin, Prakash Ishwar, Weicong Ding   +2 more
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On minimal‐node‐cost planar embeddings

Networks, 1984
AbstractThe problem of embedding an undirected graph on the planar grid is considered. Two common cost measures for this sort of problem are the area consumed by the embedding and the total length of edges in the embedding. This paper considers a third cost measure, called thenode cost measure, which is the total number of bends that are present along ...
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Node Embeddings in Social Network Analysis

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015
We introduce a distributed representation of nodes, node embeddings, in social network analysis. We compute embeddings for nodes based on their attributes and links. These embeddings can support many social network applications --- including analyses of community homogeneity, distance, and detection of community connectors (inter-community outliers ...
Thuy Vu, Douglas Stott Parker
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Matching Node Embeddings for Graph Similarity

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph kernels focus on local properties of graphs and ignore global structure. In this paper, we compare graphs based on their global properties as these are captured by the eigenvectors of their adjacency matrices.
Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis   +2 more
openaire   +1 more source

Dependable and Secure Embedded Node Demonstrator

2012
The European industry competitiveness in the embedded devices market is threatened by challenges such as cost-effectiveness, interoperability, reliability, and re-usability. This is particularly important now, when the value of embedded electronics components share in the final products is increasing, especially in ICT and health/medical equipment ...
Przemyslaw Osocha, João Carlos Cunha, Fabio Giovagnini   +2 more
openaire   +1 more source

Virtual network embedding by node-splitting

2013 15th IEEE International Conference on Communication Technology, 2013
Network virtualization, as a fundamental technology of Future Internet Architecture, has been used to overcome the ossification of the current Internet. This paper focuses on virtual nodes splitting for aggregating the diverse physical resources in the issue of virtual network embedding (VNE), which is used for the resource allocation in network ...
Jiandong Huang, Qian Wu, Yu Liu, Bing Wang, Wennai Wang   +4 more
openaire   +1 more source

Tensor Decomposition-based Node Embedding

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019
In recent years, node embedding algorithms, which learn low dimensional vector representations for nodes in a graph, have been one of the key research interests of the graph mining community. The existing algorithms either rely on computationally expensive eigendecomposition of the large matrices, or require tuning of the word embedding-based ...
Shah Muhammad Hamdi, Soukaina Filali Boubrahimi, Rafal A. Angryk   +2 more
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Learning Node Embeddings in Interaction Graphs

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
Node embedding techniques have gained prominence since they produce continuous and low-dimensional features, which are effective for various tasks. Most existing approaches learn node embeddings by exploring the structure of networks and are mainly focused on static non-attributed graphs. However, many real-world applications, such as stock markets and
Yao Zhang 0009, Yun Xiong, Xiangnan Kong, Yangyong Zhu   +3 more
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Community Based Node Embeddings for Networks

2019
Network embedding has got enormous attention in recent past for their wide range of applications across different types of networks. This paper mainly includes a simple and novel model which is used for better node embeddings with respect to community detection in social networks.
P. Meghashyam, V. Susheela Devi
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Learning Community Embedding with Community Detection and Node Embedding on Graphs

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes. We find that community embedding is not only useful for community-level applications such as graph visualization, but also beneficial to both community detection and node classification. To learn such
Sandro Cavallari, Vincent W. Zheng, Hongyun Cai 0001, Kevin Chen-Chuan Chang, Erik Cambria   +4 more
openaire   +1 more source