Assessing the effectiveness of real-world network simplification

Blagus, Neli; Šubelj, Lovro; Bajec, Marko

doi:10.1016/j.physa.2014.06.065

Cited by 14 publications

(23 citation statements)

References 57 publications

(75 reference statements)

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“…The first group quantifies how well structural properties of the original graph are preserved [46,61,72]. Popular metrics are the Degree Distribution (DD) and Clustering Coefficient Distribution (CCD) [7,45].…”

Section: Evaluation Of Graph Samplingmentioning

confidence: 99%

Preserving Minority Structures in Graph Sampling

Zhao

Jiang

Qian

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.

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Section: Evaluation Of Graph Samplingmentioning

confidence: 99%

Preserving Minority Structures in Graph Sampling

Zhao

Jiang

Qian

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…Note that RND improves the performance of the basic random node selection [7,35], where the nodes are selected to the sample uniformly at random. RND fits better spectral network properties [7] and produces the sample with larger weakly connected component [35]. Moreover, it shows good performance in preserving the clustering coefficient and betweenness centrality distribution of the original networks [35].…”

Section: Random Selectionmentioning

confidence: 99%

“…RND fits better spectral network properties [7] and produces the sample with larger weakly connected component [35]. Moreover, it shows good performance in preserving the clustering coefficient and betweenness centrality distribution of the original networks [35]. Nevertheless, it can still construct a disconnected sample network, despite a fully connected original network.…”

Section: Random Selectionmentioning

confidence: 99%

“…Sampling techniques outlined in Section 2 enable setting the size of the sampled networks in advance. We consider sample sizes of 15% of nodes from the original networks, that provides for an accurate fit of several network properties [7,35]. Table 3 and 4 present the properties of the node group structure of sampled social and information networks, respectively.…”

Section: Group Structure Of Sampled Networkmentioning

confidence: 99%

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Sampling promotes community structure in social and information networks

Blagus¹,

Šubelj²,

Weiss³

et al. 2015

Physica A: Statistical Mechanics and its Applications

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Any network studied in the literature is inevitably just a sampled representative of its real-world analogue. Additionally, network sampling is lately often applied to large networks to allow for their faster and more efficient analysis. Nevertheless, the changes in network structure introduced by sampling are still far from understood. In this paper, we study the presence of characteristic groups of nodes in sampled social and information networks. We consider different network sampling techniques including random node and link selection, network exploration and expansion. We first observe that the structure of social networks reveals densely linked groups like communities, while the structure of information networks is better described by modules of structurally equivalent nodes. However, despite these notable differences, the structure of sampled networks exhibits stronger characterization by community-like groups than the original networks, irrespective of their type and consistently across various sampling techniques. Hence, rich community structure commonly observed in social and information networks is to some extent merely an artifact of sampling.

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“…In this context, although traditionally an edge contains only information about its presence, edge weighting should be essential for efficient and effective use of GDBs. Edge weighting is also useful to visualize interpersonal communication using network graphs in order to highlight the characteristics of the relationships (Blagus et al 2014). Edges formed from the observation in interpersonal communication could reflect more varied information like temporal characteristics of interpersonal communication.…”

mentioning

confidence: 99%

Weighted network graph for interpersonal communication with temporal regularity

2017

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Over the last decade, interpersonal communication has attracted more attention from researchers than before. Although the volume of data generated through various communication devices and tools could be enormous, the recent decrease in storage cost enables us to record and store it. The analysis of interpersonal communication is useful to estimate influence in social relationships among people, to detect communities, and to recommend potential friends for users on social networking services. A network graph, which is a mathematical model that represents people as nodes and past opportunities of interpersonal communication as edges, works in such analysis. However, when the capacity of the number of edges recordable in a graph database is limited, or when only a limited number of edges is used for high-speed analysis, it is still unclear which edges should be prioritized and utilized in the analysis. Previous studies suggested that edges in network graphs can be weighted on the basis of the aggregated duration of connections, the number of connections, or the connection time. However, temporal regularity in interpersonal communication has not been well considered in the previous studies. Therefore, in this paper, we propose an edge weighting method for network graphs from interpersonal communication that determines edge weighs on the basis of the scores obtained from the spectral analysis technique. The spectral analysis technique is utilized to numerically deal with temporal regularity and frequency of interpersonal communication. An examination using real records verifies that by using our edge weighting method, link prediction works better under a condition of the limited number of edges usable for the analysis. We also deeply analyze and present the distributions of the frequencies that characterize interpersonal communication.

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Assessing the effectiveness of real-world network simplification

Cited by 14 publications

References 57 publications

Preserving Minority Structures in Graph Sampling

Preserving Minority Structures in Graph Sampling

Sampling promotes community structure in social and information networks

Weighted network graph for interpersonal communication with temporal regularity

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