Sampling from social network to maintain community structure

Summary In recent years, social recommendation between communities plays a more and more significant role in urban vehicular social networks. It has become a hot research topic due to its advantage in enhancing the efficiency of information sharing and transmission in urban vehicular social networks. This paper focuses on social recommendation between communities where achieving the optimal configuration of social recommendation is NP‐hard. We proposed an algorithm for the near optimal configuration of social recommendation between urban vehicular social communities (ANOCSR) to make this NP‐hard problem polynomially solvable based on a modified Steiner minimal tree method. Moreover, with the help of the ANOCSR, we propose another algorithm to achieve the optimal configuration of social recommendation between urban vehicular social communities subject to a global external trust value constraint (AOCSRC). We demonstrate that our algorithms are more efficient and effective than existing algorithms through extensive experiments and detailed theoretical analyses.

“…Some researchers focused on the construction of the community and its evolution. Representative work in this area includes as follows …”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Social Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

Social recommendation between urban vehicular social communities

Liu

Wang

Sun

et al. 2018

“…A network sampling method based on DLAS is reported by Rezvanian et al [7] in order to collect highly important nodes (e.g., central nodes) of graph. In [42], an improved version of forest fire sampling algorithm was proposed to decrease the scale of network data and preserve the network community structure of original network as well. Based on the basic snowball sampling, a random multiple snowball with Cohen process sampling is developed by Gao et al [41] to explore global information and local structure of the networks at the same time.…”

Section: Introductionmentioning

confidence: 99%

A new learning automata‐based sampling algorithm for social networks

Rezvanian

Meybodi

2015

Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information, and social networks. In this paper, a sampling algorithm for complex social networks that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA (eDLA) is proposed. For evaluation purpose, the eDLA-based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well-known sampling algorithms in terms of relative error and Kolmogorov-Smirnov test. It is shown that eDLA-based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA-based sampling algorithm in comparison with the DLA-based sampling algorithm has a 26.93% improvement for the average of Kolmogorov-Smirnov value for degree distribution taken over all test networks. Therefore, researchers try to study and analyze real networks such as communication, technological, information and OSNs via some metrics (i.e., centrality measures) or some techniques (i.e., network sampling methods) to estimate the characterization of these networks. Besides, some hidden important properties of networks (e.g., user age distribution, user activities, user connectivity, and many more in OSN) are discovered by studying on small sampled data. It is noted that sometimes sampling methods applied on the networks subject to preserve vital properties of the networks to reach a suitable visualization.A social network can be represented as a graph with a set of nodes such as users and a certain type of relationship between users as edges of graph. Let G = hV, Ei be a given social network graph, where V is the set of nodes and E is the set of edges. Sampled network G s = hV s , E s i is the induced subgraph of G by sampling rate ϕ, where V s ⊆ V, E s ⊆ E and 0 < ϕ < 1. It is noted that the main goal of sampling algorithms is to achieve a smaller subgraph similar to the original graph G. Sampling algorithms [8-10] play an important role in preprocessing, characterizing, studying, and analyzing the real networks. Network sampling can be applied for characterization of a small portion of a network subject to preserve main properties of the original network. For example, due to privacy restrictions of some OSN services, accessibility to the whole network information is not practical, therefore, the network is studied and characterized by estimated small samples from an important part of the original network. Taking into account large size, complexity, and dynamic properties of such real networks, most classic...

“…Enlightened by the co-ranking algorithm in [5], we take into account the concept of user community and propose UCCC for user communities and contents based on the mutually reinforcing relationship between them. Quite a few researchers have noticed the important concept of communities in social networks, such as [24] and [25]. The proposed UCCC can evaluate UGC quality from a global view instead of the personalized view in [5].…”

Section: Introductionmentioning

confidence: 99%

User communities and contents co‐ranking for user‐generated content quality evaluation in social networks

Lin

Zhai

et al. 2014

SUMMARYWith the massive popularity of social networks, more and more users can produce millions of user-generated contents (UGCs) daily. However, UGC quality is uneven, which has posed challenges to finding superior contents in such a large data set. In this paper, we present a new idea of UGC quality evaluation exploiting user communities, which are formed by users either in a friend circle or with similar interests in social networks. The intuition is that a user community can help evaluate the UGC quality better than a single user. Hence, we propose a new graph-theoretic user communities and contents co-ranking (UCCC) algorithm for UGC quality evaluation. UCCC evaluates UGCs and their related user communities simultaneously based on three different relationship networks: the network connecting UGCs, the network connecting user communities, and a third network that ties the two together. Contents and user communities are ranked following a co-ranking algorithm based on the assumption that there is a mutually reinforcing relationship between them. Experiments using real-world data have shown that UCCC outperforms competitive algorithms by a good margin in most cases and a user community is more useful than a single user for UGC quality evaluation.