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Results 1 - 10
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9,919
Optimization Mode Research of Weighted Undirected Graph
"... Abstract. An algorithm is described for constructing a weighted undirected graph structure, based on the principle of directed acyclic graph, used the improved Floyd algorithm to improve the multi-node version of the evolution of information in order to release as an indicator of difference, to imp ..."
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Abstract. An algorithm is described for constructing a weighted undirected graph structure, based on the principle of directed acyclic graph, used the improved Floyd algorithm to improve the multi-node version of the evolution of information in order to release as an indicator of difference
A shortest path algorithm for real-weighted undirected graphs
- in 13th ACMSIAM Symp. on Discrete Algs
, 1985
"... Abstract. We present a new scheme for computing shortest paths on real-weighted undirected graphs in the fundamental comparison-addition model. In an efficient preprocessing phase our algorithm creates a linear-size structure that facilitates single-source shortest path computations in O(m log α) ti ..."
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Cited by 16 (4 self)
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Abstract. We present a new scheme for computing shortest paths on real-weighted undirected graphs in the fundamental comparison-addition model. In an efficient preprocessing phase our algorithm creates a linear-size structure that facilitates single-source shortest path computations in O(m log α
A Conductance Electrical Model for Representing and Matching Weighted Undirected Graphs ∗
"... In this paper we propose a conductance electrical model to represent weighted undirected graphs that al-lows us to efficiently compute approximate graph iso-morphism in large graphs. The model is built by trans-forming a graph into an electrical circuit. Edges in the graph become conductances in the ..."
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In this paper we propose a conductance electrical model to represent weighted undirected graphs that al-lows us to efficiently compute approximate graph iso-morphism in large graphs. The model is built by trans-forming a graph into an electrical circuit. Edges in the graph become conductances
Constant Ratio Approximations of Feedback Vertex Sets in Weighted Undirected Graphs
, 1996
"... A feedback vertex set of a graph is a subset of vertices that contains at least one vertex from every cycle in the graph. We show that a feedback vertex set approximating a minimum one within a constant factor can be e ciently found in undirected graphs. In fact the derived approximation ratio match ..."
Abstract
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Cited by 6 (1 self)
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A feedback vertex set of a graph is a subset of vertices that contains at least one vertex from every cycle in the graph. We show that a feedback vertex set approximating a minimum one within a constant factor can be e ciently found in undirected graphs. In fact the derived approximation ratio
An algorithm for drawing general undirected graphs
- Information Processing Letters
, 1989
"... Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets, and entit ..."
Abstract
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Cited by 698 (2 self)
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Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets
Depth first search and linear graph algorithms
- SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depth-first search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
Abstract
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Cited by 1406 (19 self)
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of an undirect graph are presented. The space and time requirements of both algorithms are bounded by k 1V + k2E d- k for some constants kl, k2, and k a, where Vis the number of vertices and E is the number of edges of the graph being examined.
The geometry of graphs and some of its algorithmic applications
- COMBINATORICA
, 1995
"... In this paper we explore some implications of viewing graphs as geometric objects. This approach offers a new perspective on a number of graph-theoretic and algorithmic problems. There are several ways to model graphs geometrically and our main concern here is with geometric representations that res ..."
Abstract
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Cited by 524 (19 self)
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that respect the metric of the (possibly weighted) graph. Given a graph G we map its vertices to a normed space in an attempt to (i) Keep down the dimension of the host space and (ii) Guarantee a small distortion, i.e., make sure that distances between vertices in G closely match the dis-tances between
A distributed algorithm for minimum-weight spanning trees
, 1983
"... A distributed algorithm is presented that constructs he minimum-weight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm and exchange ..."
Abstract
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Cited by 435 (3 self)
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A distributed algorithm is presented that constructs he minimum-weight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
- IN ICML
, 2003
"... An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
Abstract
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Cited by 752 (14 self)
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An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning
A fast learning algorithm for deep belief nets
- Neural Computation
, 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
Abstract
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Cited by 970 (49 self)
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at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a
Results 1 - 10
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9,919
