Attribute-based edge bundling for visualizing social networks

Guo, Lin; Zuo, Wangmeng; Peng, Tao; Adhikari, Binod Kumar

doi:10.1016/j.physa.2015.06.015

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…CPM [18] greedily expands natural communities of seeds until the whole graph is covered by using a local fitness function. EdgeB-Cluster [19] bundles similar edges, adjusts the locations of nodes to optimize the visualized output of the graph and analyzes networks from a community level. Through the analysis of a consumption object in a certain region in the e-commerce platform, it was found that the number of positive comments is very large.…”

Section: Methodsmentioning

confidence: 99%

EC‐Structure: Establishing Consumption Structure through Mining E‐Commerce Data to Discover Consumption Upgrade

Guo

Zhang

2019

Complexity

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The traditional methods of analyzing consumption structure have many limitations, and data acquisition is difficult, so it is hard to scientifically verify the accuracy of algorithms. With the development of Internet economy, many scientific researchers focus on mining knowledge of consumer behavior using big data analysis technology. Because consumption decisions are influenced by not only personal characteristics but also social trends and environment, it is one-sided to analyze the impact of one single factor on the phenomenon of consumption. The authors of this paper combine the consumption structure analysis method and data processing technology using data from an e-commerce platform to extract the consumption structure of cities, compare the structural differences between different periods, and then discover consumption upgrading according to swarm intelligence. The experiments prove the efficacy of the algorithm proposed in this paper compared to other similar algorithms using several different datasets, which illustrates the algorithm’s efficacy and stable performance in consumption structure analysis.

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Section: Methodsmentioning

confidence: 99%

EC‐Structure: Establishing Consumption Structure through Mining E‐Commerce Data to Discover Consumption Upgrade

Guo

Zhang

2019

Complexity

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“…Node-based methods [10][11][12] envisage that the strong ties are formed between nodes when there is a high probability of forming triads through diferent types of relationships. Notice that nodes belong to multiple groups, but links are existent for just one dominant reason (e.g., two people linked work together or have common interests), which means that links that occupy unique clusters and nodes naturally account for multiple clusters as a result of their links.…”

Section: Introductionmentioning

confidence: 99%

Overlapping Community Detection Based on Strong Tie Detection and Non-Overlapping Link Clustering

Guo

Zhang

2022

Mathematical Problems in Engineering

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Many clustering algorithms are in favour of node-based methods, but a link between nodes has one single feature, so link-based clustering is sometimes easier than node-based methods. Being dependent on the characteristics of links, a detection algorithm for a non-overlapping link community is put forward in this paper. The method proposed also distinguishes the differences between nodes with a high degree of accuracy and detects communities with a minimal number of overlapping nodes. On the basis of three different datasets, experiments were conducted to compare the proposed algorithm with different non-overlapping and overlapping clustering algorithms, and the results show that our algorithm generates the least number of overlapping nodes and achieves a good community partition.

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“…While this effectively simplifies a drawing that reflects the data, it does not produce a simplified drawing of the data. Recently, the use of data to perform bundling has gained some attention [147,73,209,178]. However, such techniques are, we argue, mainly adaption of earlier spatial bundling methods to incorporate data elements, and not techniques for directly visualizing data that has no given spatial embedding.…”

Section: Similarity Trees For Data-driven Edge Bundlingmentioning

confidence: 99%

Visualizing multidimensional data similarities: improvements and applications

Silva

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Multidimensional datasets are increasingly more prominent and important in data science and many application domains. Such datasets typically consist of a large set of observations, or data points, each which is described by several measurements, or dimensions. During the design of techniques and tools to process such datasets, a key component is to gather insights into their structure and patterns, a goal which is targeted by multidimensional visualization methods. Structures and patterns of high-dimensional data can be described, at a core level, by the notion of similarity of observations. Hence, to visualize such patterns, we need effective and efficient ways to depict similarity relations between a large number of observations, each having a potentially large number of dimensions. Within the realm of multidimensional visualization methods, two classes of techniques exist-projections and similarity trees-which effectively capture similarity patterns and also scale well to the number of observations and dimensions of the data. However, while such techniques show similarity patterns, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we propose methods that make the computation of similarity trees efficient for large datasets, and also allow their visual explanation on a multiscale, or several levels of detail. We also propose ways to construct simplified representations of similarity trees, thereby extending their visual scalability even further. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of automatically detected groups of related observations which are also automatically annotated in terms of their similarity in the high-dimensional data space. We show next how these explanatory mechanisms can be adapted to handle both static and time-dependent multidimensional datasets. Our proposed techniques are designed to be easy to use, work nearly automatically, handle any types of quantitative multidimensional datasets and multidimensional projection techniques, and are demonstrated on a variety of real-world large datasets obtained from image collections, text archives, scientific measurements, and software engineeering.

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Attribute-based edge bundling for visualizing social networks

Cited by 7 publications

References 17 publications

EC‐Structure: Establishing Consumption Structure through Mining E‐Commerce Data to Discover Consumption Upgrade

EC‐Structure: Establishing Consumption Structure through Mining E‐Commerce Data to Discover Consumption Upgrade

Overlapping Community Detection Based on Strong Tie Detection and Non-Overlapping Link Clustering

Visualizing multidimensional data similarities: improvements and applications

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