Net-Ray: Visualizing and Mining Billion-Scale Graphs

Kang, U; Lee, Jay Yoon; Koutra, Danai; Faloutsos, Christos

doi:10.1007/978-3-319-06608-0_29

Cited by 24 publications

(20 citation statements)

References 18 publications

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“…In this paper, following such a basic idea to analyze a graph having a heavy-tailed degree distribution, we show that removing hub nodes remarkably decreases conductance values of partitions of the remaining graph. SlashBurn was applied to other related tasks including graph summarization [46] and graph visualization [47].…”

Section: B Related Workmentioning

confidence: 99%

Discovering large subsets with high quality partitions in real world graphs

Lim

Lee

Choi

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Given a real world graph, how can we find a large subgraph whose partition quality is much better than the original? Graph partitioning has received great attentions in graph mining, and especially balanced graph partitioning is required in many real world applications. However, the balanced graph partitioning is known to be NP-hard, and moreover it is known that there is no good cut at a large scale for real graphs. Due to this difficulty, in this paper, we propose a new paradigm for graph partitioning. Instead of dealing with the whole graph, our focus is on finding a large subgraph with high quality partitions, in terms of conductance. We show that removing problematic nodes, i.e. large degree nodes called hub nodes in real graphs, remarkably decreases conductance for the remaining giant connected component (GCC), while the number of nodes in the GCC is still significant. In experiments, we demonstrate that our method finds a subgraph of quite a large size with low conductance graph partitions, compared with competing methods. We also show that the competitors cannot find connected subgraphs while our method does, by construction. This improvement in partition quality for the subgraph is especially noticeable for large scale cuts-for a balanced partition, down to 14% of the original conductance with GCC size 70% of the total. As a result, the found subgraph has clear partitions at almost all scales compared with the original, and this result especially helps find communities which are well-formed, but hidden by hubs at various scales in real world graphs like social networks.

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Section: B Related Workmentioning

confidence: 99%

Discovering large subsets with high quality partitions in real world graphs

Lim

Lee

Choi

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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“…Other works on visualization tackle the issue of how to aggregate large-scale graphs while retaining important information. NetRay [22] focuses on informative visualizations of the spy plots, distribution plots, and correlation plots of web-scale graphs. Dunne and Shneiderman [23] introduce the idea of motif simplification in which common patterns of nodes and links are replaced with compact and meaningful glyphs that can portray relationships within a network more clearly while requiring less screen space to represent the network.…”

Section: Graph Visualization and Interactive Analyticsmentioning

confidence: 99%

“…GLO-STIX [7] ? FACETS [13] Voyager 2 [14] Signposts [15] NodeTrix [16] PivotSlice [17] Refinery [18] Apolo [8] OPAvion [19] NetRay [22] Motif-Simp [23] PERSEUS [9,10] PERSEUS-HUB…”

Section: Graph Visualization and Interactive Analyticsmentioning

confidence: 99%

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PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

et al. 2017

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Abstract:Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is 'important'? How can multiple researchers explore a new graph dataset collectively and "help" each other with their findings? In this article, we present PERSEUS-HUB, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. PERSEUS-HUB uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In PERSEUS-HUB, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution) marked by other users or experts. Moreover, PERSEUS-HUB guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks.

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“…Two common representations used in social network analysis are node-link (JIANU et al, 2014) diagrams and adjacency matrix views (KANG et al, 2014). Hybrids of the two have also been proposed (RUFIANGE et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

DocGenealogy – Visualizing the doctoral advisors and mentors genealogic tree

Fernandes¹,

Carvalho²

2018

Abakos

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ResumoDocGenealogy é um projeto de mineração de dados que permite a visualização e a exploração do relacionamento de assessoria / orientação de doutorados por meio de um grafo interativo. A Wikipedia é conhecida por sua natureza colaborativa informal, com base em uma forma livre e não estrutural de produzir informações. Esse é um dos seus pontos fortes, na verdade, a abertura a todo tipo de colaborações. A DocGenealogy utiliza os dados disponíveis da Wikipedia sobre assessoria e orientação para doutorados para descobrir e rastrear as relações existentes entre conselheiros ou mentores e seus alunos. O ponto de partida do processo de mineração de dados é sempre um cientista bem conhecido. Esse processo continua, até encontrar os conselheiros e mentores relacionados com renome co-relacionados, sendo construído iterativamente o grafo de relacionamento. Além disso, o DocGenealogy também mostra visualmente como as pessoas estão distribuídas por alma mater e área de conhecimento. O objetivo principal do trabalho é avaliar a eficácia de técnicas orientadas ao grafo na interpretação dos dados das infoboxes da Wikipedia. Palavras

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Net-Ray: Visualizing and Mining Billion-Scale Graphs

Cited by 24 publications

References 18 publications

Discovering large subsets with high quality partitions in real world graphs

Discovering large subsets with high quality partitions in real world graphs

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

DocGenealogy – Visualizing the doctoral advisors and mentors genealogic tree

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