Social network sampling using spanning trees

Abstract

Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves the most important properties of the original network. In this paper, we propose two sampling algorithms for sampling online social networks using spanning trees. The first proposed sampling algorithm finds several spanning trees from randomly chosen starting nodes; then the edges in these spanning trees are ranked according to the number of times that each edge has appeared in the set of found spanning trees in the given network. The sampled network is then constructed as a sub-graph of the original network which contains a fraction of nodes that are incident on highly ranked edges. In order to avoid traversing the entire network, the second sampling algorithm is proposed using partial spanning trees. The second sampling algorithm is similar to the first algorithm except that it uses partial spanning trees. Several experiments are conducted to examine the performance of the proposed sampling algorithms on well-known real networks. The obtained results in comparison with other popular sampling methods demonstrate the efficiency of the proposed sampling algorithms in terms of Kolmogorov–Smirnov distance (KSD), skew divergence distance (SDD) and normalized distance (ND).

Preserving Minority Structures in Graph Sampling

“…TIES also performed well in super pivots because of its graph induction step [3]. SST performed well in rims because the use of spanning trees maintained peripheral nodes [36].…”

“…Distributed Learning Automata Sampling (DLAS) uses multiple automata for sampling [60]. Sampling with Shortest Paths (SSP) and Sampling with Spanning Trees (SST) identify important edges to guide sampling [36,59]. Multiple Snowball with Cohen (RMSC) combines the advantages of RN and SB.…”

A Network Sampling Strategy Inspired by Epidemic Spreading

“…Finally, but not the least, the function approximation of a sample network is worthy of exploration. For example, the comparison of the epidemic spreading process on sample and original networks may be the topic of our next work [12].…”

“…(5) V S � V S ∪ w { }; (6) end while (7) for (x, y) ∈ E do (8) if x ∈ V S and y ∈ V S then (9) E S � E S ∪ (x, y) 􏼈 􏼉; (10) end if (11) end for (12) return G S � (V S , E S ); ALGORITHM 1: Contact process sampling. to that of degree distribution, the techniques with subgraph induction perform better than the corresponding techniques without subgraph induction in most datasets (except for PowerGrid and Douban).…”

Sampling on bipartite networks: a comparative analysis of eight crawling methods

“…The most popular methods include breadth first search (BFS) [12], depth first search (DFS) [12], forest-fire sampling (FFS) [2], random walk (RW) [13], snowball sampling (SNS) [14], respondent driven sampling (RDS) [15], Metropolis-Hastings random walk (MHRW) [16] and maximum-degree random walk (MDRW) [17]. Many studies have examined crawling method strategies, evaluating their dierences in terms of eciency and bias [2,10,11,18,19], how well they improve upon existing methods with certain prior knowledge [6,[20][21][22][23][24], and how they can be applied to empirically assess realworld networks, like online social platforms [11,25], P2P networks [26,27], and hidden populations [28][29][30]. These studies have focused on one-mode networks, which have only one type of nodes.…”