Network Simplification with Minimal Loss of Connectivity

Fang, Zhou; Malher, Sebastien; Toivonen, Hannu

doi:10.1109/icdm.2010.133

Cited by 46 publications

(52 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing methods for compressing static graphs generally use two types of strategies: (1) removing edges to simplify the overall graph [4,8], or (2) merging nodes that have similar properties (such as common neighbors) [5,6]. While the existing methods compress a static graph from its "spatial" (nodes or edges) perspective, in this paper we propose to compress a dynamic graph from both the "spatial" and the "temporal" perspectives simultaneously.…”

Section: Introductionmentioning

confidence: 99%

On compressing weighted time-evolving graphs

Liu

Kan

Chan

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Existing graph compression techniques mostly focus on static graphs. However for many practical graphs such as social networks the edge weights frequently change over time. This phenomenon raises the question of how to compress dynamic graphs while maintaining most of their intrinsic structural patterns at each time snapshot. In this paper we show that the encoding cost of a dynamic graph is proportional to the heterogeneity of a three dimensional tensor that represents the dynamic graph. We propose an effective algorithm that compresses a dynamic graph by reducing the heterogeneity of its tensor representation, and at the same time also maintains a maximum lossy compression error at any time stamp of the dynamic graph. The bounded compression error benefits compressed graphs in that they retain good approximations of the original edge weights, and hence properties of the original graph (such as shortest paths) are well preserved. To the best of our knowledge, this is the first work that compresses weighted dynamic graphs with bounded lossy compression error at any time snapshot of the graph.

show abstract

Section: Introductionmentioning

confidence: 99%

On compressing weighted time-evolving graphs

Liu

Kan

Chan

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

show abstract

“…Simplification approaches which maintain specific properties of the graph, like spectra [8], cuts/connectivity [9], or shortest-paths [10], [11], tend to maintain the small pairwise distances as well, which means that the resulting backbones are still hairball graphs.…”

Section: Introductionmentioning

confidence: 99%

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

Nocaj

Ortmann

Brandes

2016

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-Small-world networks have characteristically low pairwise shortest-path distances, causing distance-based layout methods to generate hairball drawings. Recent approaches thus aim at finding a sparser representation of the graph to amplify variations in pairwise distances. Since the effect of sparsification on the layout is difficult to describe analytically, the incorporated filtering parameters of these approaches typically have to be selected manually and individually for each input instance. We here propose the use of graph invariants to determine suitable parameters automatically. This allows us to perform adaptive filtering to obtain drawings in which the cluster structure is most prominent. The approach is based on an empirical relationship between input and output characteristics that is derived from real and synthetic networks. Experimental evaluation shows the effectiveness of our approach and suggests that it can be used by default to increase the robustness of force-directed layout methods.

show abstract

“…Toivonen et al [31,33], for instance, prune edges while keeping the original quality of best paths between all pairs of nodes. Here, quality is defined on path-based concepts, such as shortest path or maximum flow.…”

Section: Related Workmentioning

confidence: 99%

Sparsification of influence networks

Mathioudakis

Bonchi

Castillo

et al. 2011

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

135

View full text Add to dashboard Cite

We present Spine, an efficient algorithm for finding the "backbone" of an influence network. Given a social graph and a log of past propagations, we build an instance of the independent-cascade model that describes the propagations. We aim at reducing the complexity of that model, while preserving most of its accuracy in describing the data.We show that the problem is inapproximable and we present an optimal, dynamic-programming algorithm, whose search space, albeit exponential, is typically much smaller than that of the brute force, exhaustive-search approach. Seeking a practical, scalable approach to sparsification, we devise Spine, a greedy, efficient algorithm with practically little compromise in quality.We claim that sparsification is a fundamental datareduction operation with many applications, ranging from visualization to exploratory and descriptive data analysis. As a proof of concept, we use Spine on real-world datasets, revealing the backbone of their influence-propagation networks. Moreover, we apply Spine as a pre-processing step for the influence-maximization problem, showing that computations on sparsified models give up little accuracy, but yield significant improvements in terms of scalability.

show abstract

Network Simplification with Minimal Loss of Connectivity

Cited by 46 publications

References 17 publications

On compressing weighted time-evolving graphs

On compressing weighted time-evolving graphs

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

Sparsification of influence networks

Contact Info

Product

Resources

About