WNCG - Wireless Networking and Communications Group - graph algorithms
http://wncg.org/tags/graph-algorithms
enWNCG Student Wins Award for Paper on Epidemic Processes
http://wncg.org/news/wncg-student-wins-award-paper-epidemic-processes
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>Ph.D. student Jessica Hoffmann received second place in the INFORMS Nicholson Student Paper competition. She received the award for her recent paper "Learning Graphs from Noisy Epidemic Cascades" with her advisor, WNCG professor Constantine Caramanis.</p>
<p>Epidemics are a powerful framework for modeling human and computer viruses, as well as influence, rumors, information and disinformation. Hoffmann’s research develops algorithms to solve an inverse problem on graphs, in order to understand the precise spreading mechanisms.</p>
<p>According to the Nicholson website, "The George Nicholson Committee competition is held each year to identify and honor outstanding papers in the field of operations research and the management sciences written by a student."</p>
<p>For Hoffmann, it’s the chance to make a lasting impact that drives her work.</p>
<p>“I'm lucky in the sense that my research combines what I like to do—write rigorous (and fun!) mathematical proofs—with a feeling that the results are meaningful,” Hoffmann stated. “This particular paper shows that after an epidemic takes place you can figure out the entire underlying graph (how all the nodes are connected) based solely on an approximate knowledge of when each node became infected.” The work could expand our knowledge of how infectious diseases are spread.</p>
<p>Hoffmann is a 5<sup>th</sup>-year Ph.D. student. Her research interests include epidemic processes, graph theory, high-dimensional statistics, machine learning, and applications to large-scale networks.</p>
</div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-above"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/best-student-paper">Best Student Paper</a></div><div class="field-item odd"><a href="/tags/student-awards">Student Awards</a></div><div class="field-item even"><a href="/tags/wncg-student">WNCG student</a></div><div class="field-item odd"><a href="/tags/graph-algorithms">graph algorithms</a></div><div class="field-item even"><a href="/tags/graph-theory">graph theory</a></div></div></div><div class="field field-name-field-publish-date field-type-datetime field-label-above"><div class="field-label">Publish Date: </div><div class="field-items"><div class="field-item even"><span class="date-display-single">Thursday, December 19, 2019</span></div></div></div><div class="field field-name-field-image field-type-image field-label-above"><div class="field-label">Key Image: </div><div class="field-items"><div class="field-item even"><img src="http://wncg.org/sites/wncg.org/files/JHoffmann_key.jpg" width="600" height="430" /></div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-above"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/constantine-caramanis">Constantine Caramanis</a></div></div></div><div class="field field-name-field-related-students field-type-node-reference field-label-above"><div class="field-label">Related Researchers: </div><div class="field-items"><div class="field-item even"><a href="/people/students/jessica-hoffmann">Jessica Hoffmann</a></div></div></div><div class="field field-name-field-feature field-type-list-boolean field-label-above"><div class="field-label">Feature: </div><div class="field-items"><div class="field-item even">No</div></div></div>Thu, 19 Dec 2019 19:05:51 +0000jlu754500 at http://wncg.orghttp://wncg.org/news/wncg-student-wins-award-paper-epidemic-processes#commentsFinding dense subgraphs in massive graphs.
http://wncg.org/research/briefs/finding-dense-subgraphs-massive-graphs
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Friday, May 23, 2014</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>Given a large graph and a parameter k we are interested in detecting dense subgraphs of size k. The Densest-k-Subgraph (DkS) problem is fundamental for many applications including graph and cluster analysis, cyber-community detection and computer security and spam detection. Our research group has developed a novel algorithm with provable approximation guarantees for DkS. Our algorithm significantly outpeforms the previous state of the art for several types of real-world graphs ranging from social networks to communication graphs. Further, we implemented a distributed version of our algorithm using the MapReduce framework scaling up to 800 cores on Amazon EC2. This allowed us to find dense clusters in massive graphs with billions of edges.</p>
<p>Our paper will appear in ICML 2014.<br /><a href="http://users.ece.utexas.edu/~dimakis/DKS_ICML.pdf">http://users.ece.utexas.edu/~dimakis/DKS_ICML.pdf</a></p>
</div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/graph-algorithms">graph algorithms</a></div></div></div>Fri, 23 May 2014 08:06:21 +0000gd63663454 at http://wncg.orghttp://wncg.org/research/briefs/finding-dense-subgraphs-massive-graphs#commentsCommunity Detection in Massive Graphs
http://wncg.org/research/briefs/community-detection-massive-graphs
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Tuesday, May 6, 2014</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>WNCG Ph.D Students Dimitris Papailiopoulos and Yannis Mitliagkas, along with WNCG Professors Alex Dimakis and Constantine Caramanis, have developed an efficient low-rank framework for finding dense components of graphs with billions of connections.</p>
<p>The authors have developed a novel low-rank approximation framework that finds provably good solutions for intractable big-graph problems such as the densest k-subgraph. Their framework operates by solving smaller instances of these problems, appropriately sampled from a low-rank subspace of the graph. Their algorithm comes with novel performance bounds that depend on the graph spectrum. For most real-world graphs these bounds translate to 70%-80% approximation ratios. These guarantees are surprisingly tighter compared to worst-case approximation results, which can only guarantee a 10% approximation ratio even for moderately sized data sets.</p>
<p>A major advantage of their framework is that it runs in nearly linear time, under mild conditions on the graph. Moreover, it is scalable and parallelizable. They illustrate this by implementing it in MapReduce and by scaling out to more than 800 cores on Amazon EC2. This enables us to solve large instances of the densest k-subgraph problem on massive graphs with billions of edges. </p>
<p>For the details see: <a href="https://webspace.utexas.edu/dp26726/papers/DkS_long.pdf"> Paper </a>.</p>
<p>This work was partially supported by NSF grants CCF-1344364, CCF-1344179, DARPA XDATA and research gifts by Google and Docomo.</p>
</div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/constantine-caramanis">Constantine Caramanis</a></div><div class="field-item odd"><a href="/people/faculty/alex-dimakis">Alex Dimakis</a></div></div></div><div class="field field-name-field-related-students field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Researchers: </div><div class="field-items"><div class="field-item even"><a href="/people/students/ioannis-mitliagkas">Ioannis Mitliagkas</a></div><div class="field-item odd"><a href="/people/students/dimitris-papailiopoulos">Dimitris Papailiopoulos</a></div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/ml">ML</a>, <a href="/tags/graph-algorithms">graph algorithms</a>, <a href="/tags/machine-learning">Machine Learning</a>, <a href="/tags/statistics">Statistics</a>, <a href="/tags/computation">Computation</a>, <a href="/tags/mapreduce">MapReduce</a></div></div></div>Tue, 06 May 2014 15:14:42 +0000cc333383446 at http://wncg.orghttp://wncg.org/research/briefs/community-detection-massive-graphs#comments