Results 1 - 10 of 27,925

Sampling algorithms for pure network topologies

by Edoardo M. Airoldi, Kathleen M. Carley , 2005
"... In a time of information glut, observations about complex systems and phenomena of interest are available in several applications areas, such as biology and text. As a consequence, scientists have started searching for patterns that involve interactions among the objects of analysis, to the effect t ..."
Abstract - Cited by 23 (4 self) - Add to MetaCart
that research on models and algorithms for network analysis has become a central theme for knowledge discovery and data mining (KDD). The intuitions behind the plethora of approaches rely upon few basic types of networks, identified by specific local and global topological properties, which we term “pure

ABSTRACT Sampling Algorithms for Pure Network Topologies A Study on the Stability and the Separability of Metric Embeddings

by Edoardo M. Airoldi
"... In a time of information glut, observations about complex systems and phenomena of interest are available in several applications areas, such as biology and text. As a consequence, scientists have started searching for patterns that involve interactions among the objects of analysis, to the effect t ..."
Abstract - Add to MetaCart
that research on models and algorithms for network analysis has become a central theme for knowledge discovery and data mining (KDD). The intuitions behind the plethora of approaches rely upon few basic types of networks, identified by specific local and global topological properties, which we term “pure

Evolving Neural Networks through Augmenting Topologies

by Kenneth O. Stanley, Risto Miikkulainen - Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task ..."
Abstract - Cited by 536 (112 self) - Add to MetaCart
An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning

Modeling Internet Topology

by Kenneth Calvert, Matthew B. Doar, Ellen W. Zegura - IEEE COMMUNICATIONS MAGAZINE , 1997
"... The topology of a network, or a group of networks such as the Internet, has a strong bearing on many management and performance issues. Good models of the topological structure of a network are essential for developing and analyzing internetworking technology. This article discusses how graph-based ..."
Abstract - Cited by 493 (21 self) - Add to MetaCart
The topology of a network, or a group of networks such as the Internet, has a strong bearing on many management and performance issues. Good models of the topological structure of a network are essential for developing and analyzing internetworking technology. This article discusses how graph

Topology Control of Multihop Wireless Networks using Transmit Power Adjustment

by Ram Ramanathan , Regina Rosales-hain , 2000
"... We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints- connectivity and biconnectivity, and one optimization objective- ..."
Abstract - Cited by 688 (3 self) - Add to MetaCart
We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints- connectivity and biconnectivity, and one optimization objective

Consensus Problems in Networks of Agents with Switching Topology and Time-Delays

by Reza Olfati Saber, Richard M. Murray , 2003
"... In this paper, we discuss consensus problems for a network of dynamic agents with fixed and switching topologies. We analyze three cases: i) networks with switching topology and no time-delays, ii) networks with fixed topology and communication time-delays, and iii) max-consensus problems (or leader ..."
Abstract - Cited by 1112 (21 self) - Add to MetaCart
In this paper, we discuss consensus problems for a network of dynamic agents with fixed and switching topologies. We analyze three cases: i) networks with switching topology and no time-delays, ii) networks with fixed topology and communication time-delays, and iii) max-consensus problems (or

Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks

by Benjie Chen, Kyle Jamieson, Hari Balakrishnan, Robert Morris - ACM Wireless Networks Journal , 2001
"... ..."
Abstract - Cited by 950 (9 self) - Add to MetaCart
Abstract not found

Statistical mechanics of complex networks

by Réka Albert, Albert-lászló Barabási - Rev. Mod. Phys
"... Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as ra ..."
Abstract - Cited by 2148 (11 self) - Add to MetaCart
Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled

The link-prediction problem for social networks

by David Liben-nowell, Jon Kleinberg - J. AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY , 2007
"... Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a ne ..."
Abstract - Cited by 906 (6 self) - Add to MetaCart
network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.

GPSR: Greedy perimeter stateless routing for wireless networks

by Brad Karp, H. T. Kung - MOBICOM , 2000
"... We present Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the positions of touters and a packer's destination to make packet forwarding decisions. GPSR makes greedy forwarding decisions using only information about a router's i ..."
Abstract - Cited by 2290 (8 self) - Add to MetaCart
's immediate neighbors in the network topology. When a packet reaches a region where greedy forwarding is impossible, the algorithm recovers by routing around the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad
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