Ioanna Tsalouchidou's research while affiliated with University Pompeu Fabra and other places
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Publications (7)
Community search is a well-studied problem which, given a static graph and a query set of vertices, requires to find a cohesive (or dense) subgraph containing the query vertices. In this paper we study the problem of community search in temporal dynamic networks. We adapt to the temporal setting the notion of \emph{network inefficiency} which is ba...
![An input window graph GW\documentclass[12pt]{minimal}...](profile/Kewen-Liao/publication/333644961/figure/fig1/AS:963459684503555@1606718097037/An-input-window-graph-GWdocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
Measures of centrality of vertices in a network are usually defined solely on the basis of the network structure. In highly dynamic networks, where vertices appear and disappear and their connectivity constantly changes, we need to redefine our measures of centrality to properly capture the temporal dimension of the network structure evolution, as...
Large-scale dynamic interaction graphs can be challenging to process and store, due to their size and the continuous change of communication patterns between nodes. In this work we address the problem of summarizing large-scale dynamic graphs, while maintaining the evolution of their structure and the communication patterns. Our approach is based o...
This chapter describes how OmpSs extends the OpenMP 3.0 node programming model and how it leverages message passing interface (MPI) and OpenCL/CUDA, mastering the efficient programming of the clustered heterogeneous multi-/many-core systems that will be available in current and future computing systems. It describes the language extensions and the...
Large-scale dynamic graphs can be challenging to process and store, due to their size and the continuous change of communication patterns between nodes. In this work we address the problem of summarizing large-scale dynamic graphs, maintaining the evolution of their structure and the communication patterns. Our approach is based on grouping the nod...
Citations
... CS over static graphs has received significant research effort [11,33,40] while in many real-world complex networks such as web, social, communication, transportation, and brain networks, the interactions between objects are dynamic and subject to change over time. To this end, CS in temporal networks recently has attracted attention and several community models have been proposed [28,29,31,38,44,45,49]. However, these models suffer from three main limitations. ...
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... Users can also visualize BigQuery results using BigQuery Geo Viz and Google Earth Engine. Similarly, one can run standard SQL queries on Amazon Athena [12], Amazon Redshift [44] or Microsoft Azure for processing spatial data. ...
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... Хоорондын төв (ХТ)ийн оноо нь өндөр бол тэр агентыг сүлжээний гол төв гэж үзнэ. Энэ оноог тооцоолдог олон аргууд байдаг [6][7][8]. Сүүлийн жилүүдэд ХТ-ийн оноо өндөртэй топ к оройг илрүүлэх судалгаа эрчимжиж байна [9][10][11][12]. Гэхдээ эдгээр топ к орой сүлжээний бүтэц дэх нөлөөллийн судалгаа дутмаг байна. ...
Reference: Хоорондын төвийн аргаар сүлжээг задлах
![[object Object]](https://i1.rgstatic.net/ii/profile.image/1080234203979776-1634559312043_Q64/Kewen-Liao.jpg)
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... Dynamic graphs, where nodes or edges evolve over time, are also prevalent these days. An approach discusses the summarization of a dynamic graph based on connectivity and communication among nodes [31]. The work creates summaries of the dynamic graph over a sliding window of fixed size. ...
... Summarization of dynamic graphs: Since the dynamic nature of graphs makes lossless summarization more challenging, previous studies of dynamic graph summarization have focused largely on lossy summarization for query efficiency [9,30], social influence analysis [19,20], temporal pattern [31], etc. They aim to summarize: either the current snapshot of the input graph [9,12,30,32] or temporal patterns in the entire history of the growth of the input graph [31]. ...
Reference: Incremental Lossless Graph Summarization
