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Local Graph Partitions for Approximation and Testing
"... Abstract—We introduce a new tool for approximation and testing algorithms called partitioning oracles. We develop methods for constructing them for any class of boundeddegree graphs with an excluded minor, and in general, for any hyperfinite class of boundeddegree graphs. These oracles utilize onl ..."
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Cited by 14 (1 self)
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only local computation to consistently answer queries about a global partition that breaks the graph into small connected components by removing only a small fraction of the edges. We illustrate the power of this technique by using it to extend and simplify a number of previous approximation
Identifying useful subgoals in reinforcement learning by local graph partitioning
 In Proceedings of the TwentySecond International Conference on Machine Learning
, 2005
"... We present a new subgoalbased method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition graphs—those that are constructed using only the most recent experiences of the agent. The local scope of our subgoal discov ..."
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Cited by 69 (11 self)
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We present a new subgoalbased method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition graphs—those that are constructed using only the most recent experiences of the agent. The local scope of our subgoal
A Local Graph Partitioning Heuristic Meeting Bisection Bounds (Extended Abstract)
 8th SIAM Conf. on Parallel Processing for Scientific Computing (PP'97
, 1997
"... Abstract The problem of graph partitioning is of major importance for a broad range of applications. VLSILayout and parallel programming are only two examples where large graphs have to be cut into pieces. The partitioning of a graph into several clusters is often done by recursive bisection. Bise ..."
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Cited by 16 (3 self)
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Abstract The problem of graph partitioning is of major importance for a broad range of applications. VLSILayout and parallel programming are only two examples where large graphs have to be cut into pieces. The partitioning of a graph into several clusters is often done by recursive bisection
A local graph partitioning algorithm using heat kernel pagerank
"... Abstract. We give an improved local partitioning algorithm using heat kernel pagerank, a modified version of PageRank. For a subset S with Cheeger ratio (or conductance) h, we show that there are at least a quarter of the vertices in S that can serve as seeds for heat kernel pagerank which lead to l ..."
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Cited by 9 (1 self)
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Abstract. We give an improved local partitioning algorithm using heat kernel pagerank, a modified version of PageRank. For a subset S with Cheeger ratio (or conductance) h, we show that there are at least a quarter of the vertices in S that can serve as seeds for heat kernel pagerank which lead
Identifying Useful Subgoals in ReinforcementLearning by Local Graph Partitioning
"... Abstract We present a new method for automatically creating useful temporallyextended actions in reinforcement learning. Our method identifies states that lie between two denselyconnected regions of the state space andgenerates temporallyextended actions that take the agent efficiently to these s ..."
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to these states. We search for these states using a graph partitioning algorithm on local estimates of the transition graphthose that are constructed using only the most recent experiences of the agent. This localperspective is a key property of our algorithm and one that differentiates it from most
A fast and high quality multilevel scheme for partitioning irregular graphs
 SIAM JOURNAL ON SCIENTIFIC COMPUTING
, 1998
"... Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [Bui and Jones, Proc. ..."
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Cited by 1173 (16 self)
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Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [Bui and Jones, Proc.
Local graph partitioning as a basis for generating temporally extended actions in reinforcement learning
 International Conference on Machine Learning
, 2005
"... We present a new method for automatically creating useful temporallyextended actions in reinforcement learning. Our method identifies states that lie between two denselyconnected regions of the state space and generates temporallyextended actions (e.g., options) that take the agent efficiently to ..."
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Cited by 3 (1 self)
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to these states. We search for these states using graph partitioning methods on local views of the transition graph. This local perspective is a key property of our algorithms that differentiates it from most of the earlier work in this area, and one that allows it to scale to problems with large state spaces.
Local Graph Partitioning as a Basis for Generating TemporallyExtended Actions in Reinforcement Learning
"... We present a new method for automatically creating useful temporallyextended actions in reinforcement learning. Our method identifies states that lie between two denselyconnected regions of the state space and generates temporallyextended actions (e.g., options) that take the agent efficiently to ..."
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to these states. We search for these states using graph partitioning methods on local views of the transition graph. This local perspective is a key property of our algorithms that differentiates it from most of the earlier work in this area, and one that allows it to scale to problems with large state spaces.
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
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Cited by 801 (1 self)
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The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices
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