Results 41 to 50 of about 1,372,330 (300)
Benchmark graphs for testing community detection algorithms. [PDF]
Physical review. E, Statistical, nonlinear, and soft matter physics, 2008 Community structure is one of the most important features of real networks and reveals the internal organization of the nodes. Many algorithms have been proposed but the crucial issue of testing, i.e., the question of how good an algorithm is, with ...
Andrea Lancichinetti +2 more
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Entropy, 2021 This paper studies the problem of upper bounding the number of independent sets in a graph, expressed in terms of its degree distribution. For bipartite regular graphs, Kahn (2001) established a tight upper bound using an information-theoretic approach ...
Igal Sason
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Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW).
Yury Malkov, Dmitry A. Yashunin
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Packing Smaller Graphs into a Graph
Discrete Mathematics, 1989 AbstractLet G be a graph.
Shin-ichi Tokunaga +2 more
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Embedding Graphs into Embedded Graphs [PDF]
Algorithmica, 2020 A (possibly denerate) drawing of a graph $G$ in the plane is approximable by an embedding if it can be turned into an embedding by an arbitrarily small perturbation. We show that testing, whether a straight-line drawing of a planar graph $G$ in the plane is approximable by an embedding, can be carried out in polynomial time, if a desired embedding of ...
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Graph saturation in multipartite graphs [PDF]
Journal of Combinatorics, 2016 16 pages, 4 ...
Florian Pfender +3 more
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EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs [PDF]
AAAI Conference on Artificial Intelligence, 2019 Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs.
A. Pareja +7 more
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Graph equations for line graphs, total graphs, middle graphs and quasi-total graphs
Discrete Mathematics, 1984 AbstractLet G be a graph with vertex-set V(G) and edge-set X(G). Let L(G) and T(G) denote the line graph and total graph of G. The middle graph M(G) of G is an intersection graph Ω(F) on the vertex-set V(G) of any graph G. Let F = V′(G) ∪ X(G) where V′(G) indicates the family of all one-point subsets of the set V(G), then M(G) = Ω(F).The quasi-total ...
D. V. S Sastry, B.Syam Prasad Raju
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Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2017 This article proposes a class of dependencies for graphs, referred to as graph entity dependencies (GEDs). A GED is defined as a combination of a graph pattern and an attribute dependency. In a uniform format, GEDs can express graph functional dependencies with constant literals to catch inconsistencies, and keys ...
Fan, Wenfei, Lu, Ping
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Judgment and Decision Making, 2021 Interrupted time-series graphs are often judged by eye. Such a graph might show, for example, patient symptom severity (y) on each of several days (x) before and after a treatment was implemented (interruption).
Anthony J. Bishara +2 more
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