http://carmenere.ucsd.edu/jorge/publications/data/2012_GhCo-ejc.pdf

Distributed Strategies for Generating Weight-Balanced and Doubly Stochastic Digraphs

Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph G, DSD aims to find a subgraph D of G with the highest density (e.g., the number of edges over the number of vertices in D). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that the densest subgraph can be accurately found through a k-core (a kind of dense subgraph of G), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based- and general pattern-based density. We have performed extensive experimental evaluation on both real and synthetic datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.

/pdf/efficient-algorithms-for-densest-subgraph-discovery-jg31n4k3gb.pdf

Efficient algorithms for densest subgraph discovery

Weight-balanced and doubly stochastic digraphs are two classes of digraphs that play an essential role in a variety of cooperative control problems, including formation control, distributed averaging, and optimization. We refer to a digraph as doubly stochasticable (weight-balanceable) if it admits a doubly stochastic (weight-balanced) adjacency matrix. This paper studies the characterization of both classes of digraphs, and introduces distributed algorithms to compute the appropriate set of weights in each case.

pdf/distributed-strategies-for-generating-weight-balanced-and-1xzmpcoxck.pdf

Distributed strategies for generating weight-balanced and doubly stochastic digraphs

WiFi clients must associate to a specific Access Point (AP) to communicate over the Internet. Current association methods are based on maximum Received Signal Strength Index (RSSI) implying that a client associates to the strongest AP around it. This is a simple scheme that has performed well in purely distributed settings. Modern wireless networks, however, are increasingly being connected by a wired backbone. The backbone allows for out-of-band communication among APs, opening up opportunities for improved protocol design. This paper takes advantage of this opportunity through a coordinated client association scheme where APs consider a global view of the network, and decide on the optimal client-AP association. We show that such an association outperforms RSSI based schemes in several scenarios, while remaining practical and scalable for wide-scale deployment. We also show that optimal association is a NP-Hard problem and our max-flow based heuristic is a promising solution.

Smart Association Control in Wireless Mobile Environment Using Max-Flow

Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph G, DSD aims to find a subgraph D of G with the highest density (e.g., the number of edges over the number of vertices in D). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that a densest subgraph can be accurately found through a k-core (a kind of dense subgraph of G), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based and general pattern-based density. We have performed extensive experimental evaluation on eleven real datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.

Efficient Algorithms for Densest Subgraph Discovery

This paper presents an asynchronous distributed algorithm for solving the maximum flow problem which is based on the preflow-push approach of Golberg-Tarjan. Each node in graph initially knows the capacities of outgoing and incoming adjacent arcs, the source nodes knows additionally the number of nodes in graph. Nodes execute the same algorithm, and exchange messages with neighbors until the maximum flow is established. The algorithm is applicable in cases of multiple sources and/or targets. We give also here some ideas to adjust our algorithm to dynamic changes of arc capacities. For a graph of n nodes and m arcs, our algorithm takes O(n2m) message complexity and O(n2 ) time complexity

http://www.lifl.fr/ispdc2005/presentations/ispdc-maxflow-PHAM.pdf

A Distributed Algorithm for the Maximum Flow Problem

This paper presents a new adaptive distributed algorithm which solves the problem of finding a maximum flow in the underlying asynchronous network. Sequential processes, executing the same code over local data, exchange messages with neighbors to establish the max flow, and adapt themselves to any change of arc capacity in the network. This algorithm is derived to the case of multiple sources and/or sinks without adding virtual source and/or virtual sink. For a graph of V nodes and E arcs, the algorithm achieves O(n 2 m) message complexity and O(n 2 ) time complexity.

An adaptive distributed algorithm for the maximum flow problem in the underlying asynchronous network

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A Distributed Algorithm for the Maximum Flow Problem

An adaptive distributed algorithm for the maximum flow problem in the underlying asynchronous network