Faster Random Walks by Rewiring Online Social Networks On-the-Fly

Zhou, Zhuojie; Zhang, Nan; Gong, Zhiguo; Das, Gautam

doi:10.1145/2847526

Cited by 28 publications

(20 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhou et al [25] modify the simple random walk in order to reduce the mixing time of the walk. Boyd et al [3] considered the problem of modifying edge probabilities to achieve the fastest mixing time; this turns out to be a semi-definite program.…”

Section: Related Workmentioning

confidence: 99%

On Sampling Nodes in a Network

Chiericetti

Dasgupta

Kumar

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

View full text Add to dashboard Cite

Random walk is an important tool in many graph mining applications including estimating graph parameters, sampling portions of the graph, and extracting dense communities. In this paper we consider the problem of sampling nodes from a large graph according to a prescribed distribution by using random walk as the basic primitive. Our goal is to obtain algorithms that make a small number of queries to the graph but output a node that is sampled according to the prescribed distribution. Focusing on the uniform distribution case, we study the query complexity of three algorithms and show a near-tight bound expressed in terms of the parameters of the graph such as average degree and the mixing time. Both theoretically and empirically, we show that some algorithms are preferable in practice than the others. We also extend our study to the problem of sampling nodes according to some polynomial function of their degrees; this has implications for designing efficient algorithms for applications such as triangle counting

show abstract

Section: Related Workmentioning

confidence: 99%

On Sampling Nodes in a Network

Chiericetti

Dasgupta

Kumar

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

View full text Add to dashboard Cite

show abstract

“…One major barrier for these tasks is the absence of global information from most ONs (De Choudhury et al 2010;Zhou et al 2016). With data now being valuable digital assets and a rising awareness of privacy and security after many famous disclosures like the Cambridge Analytica scandal (Wikipedia Contributors 2019a), there are plenty of reasons for ON providers to keep details of their networks away from the public.…”

Section: Problem Motivationmentioning

confidence: 99%

“…Mostly web interface of ONs in real world only allows local neighborhood queries. Namely we only have knowledge of queried nodes set X 0 and the neighborhood n(x 0 ) of node x 0 for all nodes in X 0 (Zhou et al 2016). We also assume the knowledge of known nodes include other metadata that can be used for community affiliation decision, either explicitly or indirectly (Papagelis et al 2013).…”

Section: Graph Model Of Online Networkmentioning

confidence: 99%

See 1 more Smart Citation

Speed up random walk by leveraging community affiliation information

Yin

Zhang

2019

CCF Trans. Pervasive Comp. Interact.

Self Cite

View full text Add to dashboard Cite

Large online networks are most massive and opulent data sources these days. The inherent growing demands of analyses related data fetching conflict greatly with network providers' efforts to protect their digital assets as well as users' increasing awareness of privacy. Restrictions on web interfaces of online networks prevent third party researchers from gathering sufficient data and further global images of these networks are also hidden. Under such circumstances, only techniques like random walk approaches that can run under local neighborhood access will be adopted to fulfill large online network sampling tasks. Meanwhile, the presence of highly clustered community like structure in large networks leads to random walk's poor conductance, causing intolerable and hard-to-foresee long mixing time before useful samples can be collected. With lack of techniques incorporate online network topology features being the context, in this paper we focus on taking use of community affiliation information that possibly comes with metadata when querying objects in online networks, and proposed a speeded version of random walk by raising the probability of inter-community edges being selected. Assuming the community structure is well established as promised, the community speeded random walk expects better conductance and faster convergence. Our method forces the sampler to travel rapidly among different communities that conquers the bottlenecks and thus the samples being collected are of higher quality. We also consider the scenario when community affiliation is not directly available, where we apply feature selection algorithms to select features as community.

show abstract

“…A desirable property of random walk is that it asymptotically converges (as it grows longer) to a stationary sampling distribution that can be derived without knowledge of the graph topology (e.g., with probability proportional to a node's degree for simple random walk [18], or uniform distribution for Metropolis-Hastings random walk [18]). The number of steps required for a random walk to reach this stationary distribution is the "burn-in" period which, unfortunately, is often quite long for real-world social networks [23,7].…”

Section: Existing Techniques and Their Problemsmentioning

confidence: 99%

Leveraging history for faster sampling of online social networks

2015

Self Cite

View full text Add to dashboard Cite

With a vast amount of data available on online social networks, how to enable efficient analytics has been an increasingly important research problem. Many existing studies resort to sampling techniques that draw random nodes from an online social network through its restrictive web/API interface. While almost all of these techniques use the exact same underlying technique of random walk -a Markov Chain Monte Carlo based method that iteratively transits from one node to its random neighbor.Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i.e., no access to the full graph topology). A problem with random walks, however, is the "burn-in" period which requires a large number of transitions/queries before the sampling distribution converges to a stationary value that enables the drawing of samples in a statistically valid manner.In this paper, we consider a novel problem of speeding up the fundamental design of random walks (i.e., reducing the number of queries it requires) without changing the stationary distribution it achieves -thereby enabling a more efficient "drop-in" replacement for existing sampling-based analytics techniques over online social networks. Technically, our main idea is to leverage the history of random walks to construct a higher-ordered Markov chain. We develop two algorithms, Circulated Neighbors and Groupby Neighbors Random Walk (CNRW and GNRW) and rigidly prove that, no matter what the social network topology is, CNRW and GNRW offer better efficiency than baseline random walks while achieving the same stationary distribution. We demonstrate through extensive experiments on real-world social networks and synthetic graphs the superiority of our techniques over the existing ones.

show abstract

Faster Random Walks by Rewiring Online Social Networks On-the-Fly

Cited by 28 publications

References 25 publications

On Sampling Nodes in a Network

On Sampling Nodes in a Network

Speed up random walk by leveraging community affiliation information

Leveraging history for faster sampling of online social networks

Contact Info

Product

Resources

About