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Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Graph embedding aims to encode nodes/edges into low-dimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph ...
Xiaobin Hong 0002 +6 more
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Graph embedding aims to encode nodes/edges into low-dimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph ...
Xiaobin Hong 0002 +6 more
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Simultaneous Embedding of Embedded Planar Graphs
International Journal of Computational Geometry & Applications, 2011A simultaneous embedding with fixed edges (SEFE) of a set of k planar graphs G1,…,Gk on the same set of vertices is a set of k planar drawings of G1,…,Gk, respectively, such that each vertex is placed on the same point in all the drawings and each edge is represented by the same Jordan curve in the drawings of all the graphs it belongs to.
Patrizio Angelini +2 more
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Embedding graphs in Cayley graphs
Graphs and Combinatorics, 1987Over ten years ago Babai showed that for any graph Y and for any sufficiently large group G, there is a Cayley graph X of G such that Y is an induced subgraph of X. The bounds given by him for \(| G|\) have been recently reduced by Babai and Sós to approximately \(9.5| Y|^ 3\).
Chris D. Godsil, Wilfried Imrich
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Graph Ear Decompositions and Graph Embeddings
SIAM Journal on Discrete Mathematics, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianer Chen, Saroja P. Kanchi
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Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
Graph data are prevalent in communication networks, social media, and biological networks. These data, which are often noisy or inexact, can be represented by uncertain graphs, whose edges are associated with probabilities to indicate the chances that they exist.
Jiafeng Hu +4 more
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Graph data are prevalent in communication networks, social media, and biological networks. These data, which are often noisy or inexact, can be represented by uncertain graphs, whose edges are associated with probabilities to indicate the chances that they exist.
Jiafeng Hu +4 more
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The embeddings of a graph—A survey
Journal of Graph Theory, 1978AbstractTopological graph theory seeks to find answers to the question of how graphs map into surfaces. This paper surveys the information now available about the range of a graph, namely, the set of surfaces on which the graph can be “neatly” embedded.
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Embeddings of bipartite graphs
Journal of Graph Theory, 1983AbstractIf G is a bipartite graph with bipartition A, B then let Gm,n(A, B) be obtained from G by replacing each vertex a of A by an independent set a1, …, am, each vertex b of B by an independent set b1,…, bn, and each edge ab of G by the complete bipartite graph with edges aibj (1 ≤ i ≤ m and 1 ≤ j ≤ n).
Mohammed Abu-Sbeih, Torrence D. Parsons
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On the Inference of Original Graph Information from Graph Embeddings
ACM Transactions on Sensor NetworksGraph embedding converts a graph data into a low dimensional space to preserve the original graph information. However, graph data can be reconstructed by malicious adversaries to train machine learning models from graph embeddings. This paper studies to
Yantao Li, Xinyu Lei, Gang Zhou
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Embedding graphs onto the Supercube
IEEE Transactions on Computers, 1995Summary: We consider the supercube, a new interconnection network derived from the hypercube. The supercube, introduced by Sen, has the same diameter and connectivity as a hypercube but can be realized for any number of nodes, not only powers of 2. We study the supercube's ability to execute parallel programs, using graph-embedding techniques.
AULETTA, Vincenzo +2 more
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On Lipschitz Embeddings of Graphs
2008In pattern recognition and related fields, graph based representations offer a versatile alternative to the widely used feature vectors. Therefore, an emerging trend of representing objects by graphs can be observed. This trend is intensified by the development of novel approaches in graph based machine learning, such as graph kernels or graph ...
Kaspar Riesen, Horst Bunke
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