Social Structure of Facebook Networks

Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A.

doi:10.2139/ssrn.1470768

Cited by 220 publications

(320 citation statements)

References 53 publications

Supporting

Mentioning

307

Contrasting

Unclassified

Order By: Relevance

“…Facebook social networks of California Universities. Using the space of graphlets of size k = 4, Caltech is noticeably different than others, which is consistent with the findings in (Traud et al, 2012).…”

Section: Applicationssupporting

confidence: 88%

“…In our case, we use k = 4. The figure shows Caltech noticeably different than others, consistent with the results in (Traud et al, 2012) which shows how Caltech is well-known to be organized almost exclusively according to its undergraduate "Housing" residence system, in contrast to other schools that follow the predominant "dormitory" residence system. The residence system seems to impact the organization of the social community structures at Caltech.…”

Section: Large-scale Graph Comparison and Classificationsupporting

confidence: 86%

“…In this section, we show how graphlets could be useful for network analysis, anomaly detection, and graph classification. First, we study the full data set of Facebook100, which contains 100 Facebook networks that represent a variety of US schools (Traud et al, 2012). We plot the GFD (i.e., graphlet frequency distribution) score pictorially in Figure 6 for all California schools.…”

Section: Large-scale Graph Comparison and Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Graphlet decomposition: framework, algorithms, and applications

et al. 2016

View full text Add to dashboard Cite

Abstract. From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with millions of nodes and edges, which impedes the application of graphlets to new problems that require large-scale network analysis. To address these problems, we propose a fast, efficient, and parallel algorithm for counting graphlets of size k = {3, 4}-nodes that take only a fraction of the time to compute when compared with the current methods used. The proposed graphlet counting algorithms leverages a number of proven combinatorial arguments for different graphlets. For each edge, we count a few graphlets, and with these counts along with the combinatorial arguments, we obtain the exact counts of others in constant time. On a large collection of 300+ networks from a variety of domains, our graphlet counting strategies are on average 460x faster than current methods. This brings new opportunities to investigate the use of graphlets on much larger networks and newer applications as we show in the experiments. To the best of our knowledge, this paper provides the largest graphlet computations to date as well as the largest systematic investigation on over 300+ networks from a variety of domains.

show abstract

Section: Applicationssupporting

confidence: 88%

Section: Large-scale Graph Comparison and Classificationsupporting

confidence: 86%

Section: Large-scale Graph Comparison and Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Graphlet decomposition: framework, algorithms, and applications

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Firstly, we present our experimental results on synthetic graphs from the LFR benchmark [11,12]. Secondly, we evaluate our algorithms on 100 Facebook friendship networks [34]. We start by describing our experimental setup and the used scoring and then continue with the results on the synthetic and the real-world networks.…”

Section: Methodsmentioning

confidence: 99%

Local Community Detection Based on Small Cliques

2017

View full text Add to dashboard Cite

Community detection aims to find dense subgraphs in a network. We consider the problem of finding a community locally around a seed node both in unweighted and weighted networks. This is a faster alternative to algorithms that detect communities that cover the whole network when actually only a single community is required. Further, many overlapping community detection algorithms use local community detection algorithms as basic building block. We provide a broad comparison of different existing strategies of expanding a seed node greedily into a community. For this, we conduct an extensive experimental evaluation both on synthetic benchmark graphs as well as real world networks. We show that results both on synthetic as well as real-world networks can be significantly improved by starting from the largest clique in the neighborhood of the seed node. Further, our experiments indicate that algorithms using scores based on triangles outperform other algorithms in most cases. We provide theoretical descriptions as well as open source implementations of all algorithms used.

show abstract

“…We thus look for real traces of social networks that have the same characteristics as the one exhibited in [6]. We found that the Facebook social network of the Smith College (MA) [8] is very close to Xfire, especially the average social degree (around 65 connections per user). This social network contains 2,970 users.…”

Section: Simulation Settingmentioning

confidence: 99%

Resource allocation in underprovisioned multioverlay live video sharing services

Liu

Ahmad

Buyukkaya

et al. 2012

Proceedings of the 2012 ACM Workshop on Capacity Sharing

View full text Add to dashboard Cite

In a multioverlay live video sharing service consisting of multiple independent peer-to-peer live video streaming systems, a user can simultaneously watch multiple live video streams. A major challenge for such services is the interoverlay bandwidth competition problem, which is to find an upload bandwidth allocation between the overlays each peer has subscribed to. So far, no solution has been proposed in the literature for the important case where the overall system is underprovisioned, that is, when peers do not have enough upload bandwidth to ensure a distribution of videos at full quality. We show that an allocation of upload resources that minimizes the wastage of resources (i.e., minimizes the upload bandwidth allocated to overprovisioned overlays) can be computed in polynomial time. Then we present a generic model that allows the design of different strategies for the management of the resource deficit in underprovisioned systems. Finally, we provide relevant simulation results to demonstrate the gains in video quality resulting from the implementation of our solutions.

show abstract

Social Structure of Facebook Networks

Cited by 220 publications

References 53 publications

Graphlet decomposition: framework, algorithms, and applications

Graphlet decomposition: framework, algorithms, and applications

Local Community Detection Based on Small Cliques

Resource allocation in underprovisioned multioverlay live video sharing services

Contact Info

Product

Resources

About