By using social media, many companies try to exploit new forms of interaction, collaboration, and knowledge sharing through leveraging the social, collaborative dimension of social software. The traditional collective knowledge management model based on a top-down approach is now opening up new avenues for a bottom-up approach incorporating a more personal knowledge management dimension, which could be synergized into collective knowledge using the social-collaborative dimension of social media. This article addresses the following questions: (1) How can social media support the management of personal and collective knowledge using a synergetic approach? (2) Do the personal and collective dimensions compete with each other, or can they reinforce each other in a more effective manner using social media? Our findings indicate that social media supports both the personal and collective dimensions of knowledge, while integrating a social collaborative dimension. The article introduces a framework that classif...

/pdf/social-media-in-organizations-leveraging-personal-and-4zz5va7qyg.pdf

Social Media in Organizations: Leveraging Personal and Collective Knowledge Processes

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

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

We present a new algorithm that solves the problem of distributively determining the maximum flow in an asynchronous network. This distributed algorithm is based on the preflow-push technique. Sequential processes, executing the same code over local data, exchange messages with neighbors to establish the max flow. This algorithm is derived to the case of multiple sources and/or sinks without modifications. For a network of n nodes and m arcs, the algorithm achieves O(n2m) message complexity and O(n2) time complexity.

A distributed preflow-push for the maximum flow problem

Proceedings of the 5th international conference on Innovative Internet Community Systems

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Marc Bui

Author Tools

Chat about Author

Papers

A Distributed Algorithm for the Maximum Flow Problem

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

A distributed preflow-push for the maximum flow problem

Proceedings of the 5th international conference on Innovative Internet Community Systems