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On finding common neighborhoods in massive graphs

Adam L. Buchsbaum, Raffaele Giancarlo, Jeffery R. Westbrook
<span title="">2003</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/elaf5sq7lfdxfdejhkqbtz6qoq" style="color: black;">Theoretical Computer Science</a> </i> &nbsp;
We consider the problem of ÿnding pairs of vertices that share large common neighborhoods in massive graphs.  ...  any pair of vertices with a large common neighborhood must essentially store and process the input graph o line.  ...  Introduction We study the problem of ÿnding pairs of vertices with large common neighborhoods in a directed graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0304-3975(02)00569-8">doi:10.1016/s0304-3975(02)00569-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7kngisrfqjdkdajs4ogtizpkla">fatcat:7kngisrfqjdkdajs4ogtizpkla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190415140950/https://core.ac.uk/download/pdf/82276740.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/e3/c3e324de979b8d55f437e248251995fc26689c16.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0304-3975(02)00569-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Page 8164 of Mathematical Reviews Vol. , Issue 2004j [page]

<span title="">2004</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
On finding common neighborhoods in massive graphs. (English summary) Theoret. Comput. Sci. 299 (2003), no. 1-3, 707-718.  ...  This paper considers the problem of determining if there are pairs of vertices with “large” common neighborhood in a “massivegraph.  ... 
<span class="external-identifiers"> </span>
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New results for finding common neighborhoods in massive graphs in the data stream model

A.L. Buchsbaum, R. Giancarlo, B. Racz
<span title="">2008</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/elaf5sq7lfdxfdejhkqbtz6qoq" style="color: black;">Theoretical Computer Science</a> </i> &nbsp;
We consider the problem of finding pairs of vertices that share large common neighborhoods in massive graphs.  ...  We give lower bounds for randomized, two-sided error algorithms that solve this problem in the data-stream model of computation. Our results correct and improve those of  ...  The second author was partially supported by the Italian FIRB project ''Bioinformatica per la Genomica e la Proteomica'' and by MIUR FIRB Italy-Israel project ''Pattern Matching and Discovery in Discrete  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.tcs.2008.06.056">doi:10.1016/j.tcs.2008.06.056</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/net5jpdmzjeapcjieatxw5mq3q">fatcat:net5jpdmzjeapcjieatxw5mq3q</a> </span>
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Scalable Compression of a Weighted Graph [article]

Kifayat Ullah Khan, Waqas Nawaz, Young-Koo Lee
<span title="2016-11-10">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
All the aforementioned interactions from various domains are represented as edge weights in a graph.  ...  Supporting such real life interactions produce a knowledge rich massive repository of data. However, efficiently understanding underlying trends and patterns is hard due to large size of the graph.  ...  Therefore, underlying graph is a rich and large repository due to real world interactions. However, efficiently finding useful knowledge from a graph is hard, due to its massive size.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.03159v1">arXiv:1611.03159v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jjlke7hzifcale7en7mrvuw5jm">fatcat:jjlke7hzifcale7en7mrvuw5jm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191019195505/https://arxiv.org/pdf/1611.03159v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/52/2b/522bf4ee81cc7b114e6644b3fbc1d53bb656cb19.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.03159v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

OPAvion

Leman Akoglu, Duen Horng Chau, U. Kang, Danai Koutra, Christos Faloutsos
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vxrc3vebzzachiwy3nopwi3h5u" style="color: black;">Proceedings of the 2012 international conference on Management of Data - SIGMOD &#39;12</a> </i> &nbsp;
OPAvion consists of three modules: (1) The Summarization module (Pegasus) operates off-line on massive, diskresident graphs and computes graph statistics, like PageRank scores, connected components, degree  ...  In our demonstration, we invite our audience to interact with OPAvion and try out its core capabilities on the Stack Overflow Q&A graph that describes over 6 million questions and answers among 650K users  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2213836.2213941">doi:10.1145/2213836.2213941</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sigmod/AkogluCKKF12.html">dblp:conf/sigmod/AkogluCKKF12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oj7arrwbvzf33ivfphz2llib4e">fatcat:oj7arrwbvzf33ivfphz2llib4e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170814000327/http://web.eecs.umich.edu/~dkoutra/papers/sigmod12demo-opavion.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ce/06/ce06167ca14f8d190bdc3371d285e0db99aed56b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2213836.2213941"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Scalable discovery of best clusters on large graphs

Kathy Macropol, Ambuj Singh
<span title="2010-09-01">2010</span> <i title="VLDB Endowment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p6rqwwpkkjbcldejepcehaalby" style="color: black;">Proceedings of the VLDB Endowment</a> </i> &nbsp;
However, finding these clusters can be difficult as graph sizes increase. Most current graph clustering algorithms scale poorly in terms of time or memory.  ...  In addition, the clusters returned by TopGC are consistently found to be better both in calculated score and when compared on real world benchmarks.  ...  One recent paper has focused on finding the top-k maximal cliques in uncertain graphs [32] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14778/1920841.1920930">doi:10.14778/1920841.1920930</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dcdg7tdpyfbzrms4c5tqauyfoy">fatcat:dcdg7tdpyfbzrms4c5tqauyfoy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120506020159/http://vldb.org/pvldb/vldb2010/pvldb_vol3/R62.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b3/06/b30631f0bafbe7a606b81e0c7d336674d93aa113.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14778/1920841.1920930"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Subgraph Isomorphism Search in Massive Graph Databases

Chemseddine Nabti, Hamida Seba
<span title="">2016</span> <i title="SCITEPRESS - Science and and Technology Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fegcbmwmbnchtgfrpifyz7f4su" style="color: black;">Proceedings of the International Conference on Internet of Things and Big Data</a> </i> &nbsp;
We propose to use compressed graphs. In our approach, subgraph isomorphism search is achieved on compressed representations of graphs without decompressing them.  ...  They explore a large search space which results in a high computational cost when we deal with massive graph data. To reduce time and memory space complexity of subgraph isomorphism search.  ...  ∀(h(u), h(v)) ∈ E(G 2 ) : (u, v) ∈ E(G 1 ) and 2 ((h(u), h(v))) = 1 ((u, v)) In exact matching we can find also other forms like maximum common subgraph, monomorphism, and homomorphism. • The maximum common  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5220/0005875002040213">doi:10.5220/0005875002040213</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iotbd/NabtiS16.html">dblp:conf/iotbd/NabtiS16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cfcrguampravjmgqwiapfvhf4q">fatcat:cfcrguampravjmgqwiapfvhf4q</a> </span>
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Extracting the Core Structure of Social Networks Using (α, β)-Communities

Liaoruo Wang, John Hopcroft, Jing He, Hongyu Liang, Supasorn Suwajanakorn
<span title="">2013</span> <i title="Internet Mathematics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/btilruk4pjav5n4cm5wszidqtm" style="color: black;">Internet Mathematics</a> </i> &nbsp;
Further, similar experiments on random graphs demonstrate that the core structure found in many social networks is due to their underlying social structure, rather than to high-degree vertices or a particular  ...  In this paper, we present a heuristic algorithm that in practice successfully finds a fundamental community structure. We also explore the structure of (α, β)-communities in various social networks.  ...  The social network based on common interest shared by Slashdot users was obtained and released in February 2009 in [Leskovec et al. 08] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/15427951.2012.678187">doi:10.1080/15427951.2012.678187</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sdcd5gn7rjg4lfanwdkzd4klmi">fatcat:sdcd5gn7rjg4lfanwdkzd4klmi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180723150206/http://www.internetmathematicsjournal.com/api/v1/articles/1533-extracting-the-core-structure-of-social-networks-using-communities.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b0/70/b0704a1100c270b74338fa4155db661918a7eadd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/15427951.2012.678187"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Parallel Community Detection Algorithm for Big Social Networks

Yathrib AlQahtani, Mourad Ykhlef
<span title="">2018</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
DenGraph is one of the density-based algorithms that used to find clusters of arbitrary shapes based on users' interactions in social networks.  ...  In this article, DenGraph algorithm has been redesigned to work in distributed computing environment. We proposed ParaDengraph Algorithm based on Pregel parallel model for large graph processing.  ...  First, compute the ε-neighborhood to determine core and non-core nodes. Then, generate a new graph of the core nodes. Then, clusters are identified by finding connected components in the core graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2018.090146">doi:10.14569/ijacsa.2018.090146</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mu4bfdqb2rd77l3nmfq3q3q6ie">fatcat:mu4bfdqb2rd77l3nmfq3q3q6ie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720052548/http://thesai.org/Downloads/Volume9No1/Paper_46-A_Parallel_Community_Detection_Algorithm.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/55/bb/55bb04bbab15b0c44a59e4759733ba1ced998559.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2018.090146"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Set-based approach for lossless graph summarization using Locality Sensitive Hashing

Kifayat Ullah Khan
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vuw5ktdyknehrehttg5qm4rfqm" style="color: black;">2015 31st IEEE International Conference on Data Engineering Workshops</a> </i> &nbsp;
Graph summarization is a valuable approach for in-memory processing of a big graph.  ...  Total number of minutes spent on Facebook each month: 640 Million.  ...  To find the SSNs, we apply LSH on G using steps in Figure 2 , and retrieve CSSNs against each q.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdew.2015.7129586">doi:10.1109/icdew.2015.7129586</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icde/Khan15.html">dblp:conf/icde/Khan15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jtdpvfng35a3tid5jaql4u3gm4">fatcat:jtdpvfng35a3tid5jaql4u3gm4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811234257/http://www.cse.ust.hk/icdephd15/papers/set.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d8/31/d831cd161d30bfa7eb9bd788b2aa79ff817a5245.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdew.2015.7129586"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Estimation of Graphlet Counts in Massive Networks

Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
<span title="">2018</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/j6amxna35bbs5p42wy5crllu2i" style="color: black;">IEEE Transactions on Neural Networks and Learning Systems</a> </i> &nbsp;
Most previous work has focused on exact algorithms; however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually  ...  Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.  ...  Commons Attribution 3.0 License.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2018.2826529">doi:10.1109/tnnls.2018.2826529</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29994543">pmid:29994543</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ckfw6llcijdmrivjz7h2omaici">fatcat:ckfw6llcijdmrivjz7h2omaici</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108151128/https://ieeexplore.ieee.org/ielx7/5962385/8585323/08361082.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a9/60/a960d4405a1aea8807154a7d91b9a96391078bfe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2018.2826529"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Estimation of Graphlet Statistics [article]

Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
<span title="2017-02-28">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Most previous work has focused on exact algorithms, however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually  ...  Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.  ...  This ensures we avoid common problems present in other approaches such as the curse of the last reducer [38] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1701.01772v2">arXiv:1701.01772v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oym3iijdcvdenftzki4hd3ccby">fatcat:oym3iijdcvdenftzki4hd3ccby</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826233133/https://arxiv.org/pdf/1701.01772v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/17/b0/17b04367e86f7710beb76a15dbcbf8d1243e2fae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1701.01772v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Connectivity structure of bipartite graphs via the KNC-plot

Ravi Kumar, Andrew Tomkins, Erik Vee
<span title="">2008</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/puezkhxc3rggrgb456avsvxi34" style="color: black;">Proceedings of the international conference on Web search and web data mining - WSDM &#39;08</a> </i> &nbsp;
Given a bipartite graph G = (U, V, E), we say that two nodes in U are kneighbors if there exist at least k distinct length-two paths between them; this defines a k-neighborhood graph on U where the edges  ...  For example, in a bipartite graph of users and interests, two users are k-neighbors if they have at least k common interests.  ...  Another line of work that is related to ours is that of finding dense subgraphs in massive graphs. Kumar et al. [17] studied the problem of finding dense communities in the web graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1341531.1341550">doi:10.1145/1341531.1341550</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsdm/KumarTV08.html">dblp:conf/wsdm/KumarTV08</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/avdh4437yfdy5ctoptmwsx4cru">fatcat:avdh4437yfdy5ctoptmwsx4cru</a> </span>
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Spatiotemporal Neighborhood Discovery for Sensor Data [chapter]

Michael P. McGuire, Vandana P. Janeja, Aryya Gangopadhyay
<span title="">2010</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The purpose of the spatiotemporal neighborhoods is to provide regions in the data where knowledge discovery tasks such as outlier detection, can be focused.  ...  ABSTRACT The focus of this paper is the discovery of spatiotemporal neighborhoods in sensor datasets where a time series of data is collected at many spatial locations.  ...  Acknowledgements This article has been funded in part by the National Oceanic and Atmospheric Administration (Grants NA06OAR4310243 and NA07OAR4170518).  ... 
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Author index

<span title="">2003</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/elaf5sq7lfdxfdejhkqbtz6qoq" style="color: black;">Theoretical Computer Science</a> </i> &nbsp;
Westbrook, On finding common neighborhoods in massive graphs (1-3) 707-718 Chen, Z.-Z., T. Jiang, G. Lin, J. Wen, D. Xu, J. Xu and Y.  ...  Silvestri, The minimum broadcast range assignment problem on linear multi-hop wireless networks (Notes) (1-3) 751-761 Courcelle, B., The monadic second-order logic of graphs XIV: uniformly sparse graphs  ... 
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