Adaptive Disentanglement Based on Local Clustering in Small-World Network Visualization

Nocaj, Arlind; Ortmann, Mark; Brandes, Ulrik

doi:10.1109/tvcg.2016.2534559

Cited by 16 publications

(13 citation statements)

References 33 publications

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“…An interesting task for future research is a comparision of the proposed measures with measures from graph drawing (Melanon an Sallaberry, 2008;Nocaj et al, 2015Nocaj et al, , 2016.…”

Section: Discussionmentioning

confidence: 99%

Corrected Overlap Weight and Clustering Coefficient

Batagelj

2019

Lecture Notes in Social Networks

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We discuss two well known network measures: the overlap weight of an edge and the clustering coefficient of a node. For both of them it turns out that they are not very useful for data analytic task to identify important elements (nodes or links) of a given network. The reason for this is that they attain their largest values on maximal subgraphs of relatively small size that are more probable to appear in a network than that of larger size. We show how the definitions of these measures can be corrected in such a way that they give the expected results. We illustrate the proposed corrected measures by applying them on the US Airports network using the program Pajek.A direct measure of the overlap of an edge e = (u : v) ∈ E in an undirected simple graph G = (V, E) is the number of common neighbors of its end nodes u and v (see Figure 1). It is equal to t(e) -the number of triangles (cycles of length 3) to which the edge e belongs. The edge neighbors subgraph is labeled T (deg(u) − t(e) − 1, t(e), deg(v) − t(e) − 1) -the subgraph in Figure 1 is labeled T (4, 5, 3). There are two problems with this measure:• it is not normalized (bounded to [0, 1]);• it does not consider the 'potentiality' of nodes u and v to form triangles -there are min(deg(u), deg(v)) − 1 − t(e) nodes in the smaller set of neighbors that are not in the other set of neighbors.

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“…An interesting task for future research is a comparision of the proposed measures with measures from graph drawing (Melanon an Sallaberry, 2008;Nocaj et al, 2015Nocaj et al, , 2016.…”

Section: Discussionmentioning

confidence: 99%

Corrected Overlap Weight and Clustering Coefficient

Batagelj

2019

Lecture Notes in Social Networks

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“…The use of community structures has a long history in graph visualisation, e.g. [8], where each community is laid out separately before being composed to complete the layout, or [9], where community structures are used to inform edge sparsification and thus render visualisation tractable. Here the idea is to bundle together all the edges between pairs of communities and use additional vertices, one per community, as a link between intercommunity and intra-community edges.…”

Section: The Compound Graphmentioning

confidence: 99%

“…4. On the left is a compound graph visualisation of the Gnutella06 peer-to-peer network 6 , with 8,717 vertices and 31,525 edges, [14]; on the right is the Smith60 7 network from the Facebook100 dataset with 2,970 vertices and 97,133 edges, [15] (a visualisation of this graph also appears in [9], Fig. 11).…”

Section: Wider Applicability Of the Compound Graphmentioning

confidence: 99%

The Compound Graph: A Case Study for Community Visualisation in Social Networks

Walshaw

2019

2019 23rd International Conference Information Visualisation (IV)

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This paper builds on previous work which aimed at providing a graph-based visual exploration of melodic relationships (tune families) within collections of traditional music. Here, using a community detection algorithm, potential tune families can be readily identified. However, the richer the information contained in the graph, the more difficult it is for the visualisation algorithms to operate successfully. Therefore, an approach is proposed which uses modified versions of the graph both to enhance the community detection results and, more importantly, restructure the graph, by creating a compound graph, to reveal the communities visually. Finally, the wider applicability of the technique is considered.

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“…This shortcomming will be shown in our experiments. Second, as the visualization quality is sensitive to the parameters, such as sampling rate, it is difficult to find an appropriate value to obtain a satisfactory layout result [10]. In detail, centrality based backbone method lost too much topological information because they simply used a tree to organize the backbone, which overly simplified the backbone.…”

Section: Introductionmentioning

confidence: 99%

IES-Backbone: An Interactive Edge Selection Based Backbone Method for Small World Network Visualization

et al. 2019

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Visualization of the small world network is an excellent challenge for classic layout algorithm, which is highly connected, resulting in the shape of the hairball. Backbone extraction method can simplify the classic layout to get better visualization, and it has become a significant approach in this field. However, contemporary approaches have two primary defects. Centrality based method loses plenty of topology information, and most contemporary approaches have the problem in low interactivity due to parameter sensibility. We proposed a backbone method based on interactive edge selection (IES-Backbone) to solve two problems for small world network visualization that mentioned above. The proposed method starts with backbone extraction of the network and then apply the layout algorithm to get visualization results. A critical approach of the backbone method is edge selection, which is based on the distance between vertices layout of the binary stress model. Edge selection makes the simplified network a clear community structure feature with more topological details. The simplified network is high in homophily and has closer average path length to the original network. The visualization result is controlled by edge limit ratio r and sampling rate s. Different choices of two parameters can change the results substantially on visual without affecting the layout quality, which proves high interactivity for users. Experiments prove that IES-Backbone is an interactive visualization method that presents community and sufficient topological features. INDEX TERMS Visualization, graph layout, small world network, backbone.

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Adaptive Disentanglement Based on Local Clustering in Small-World Network Visualization

Cited by 16 publications

References 33 publications

Corrected Overlap Weight and Clustering Coefficient

Corrected Overlap Weight and Clustering Coefficient

The Compound Graph: A Case Study for Community Visualisation in Social Networks

IES-Backbone: An Interactive Edge Selection Based Backbone Method for Small World Network Visualization

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