A topic community‐based method for friend recommendation in large‐scale online social networks

Abstract

Online social networks (OSNs) have become more and more popular and have attracted a great many users. Friend recommendation, which is one of the important services in OSN, can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider either user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via Nonnegative Matrix Factorization (NMF). This method first applies joint NMF model to mine topic communities existing in OSN by combing link and content information. Then it computes user pairwise similarities and makes friends recommendation based on topic communities. Furthermore, this method can be implemented using the MapReduce distributed computing framework. Extensive experiments show that our proposed method not only has better recommendation performance than state-of-the-art methods but also has good scalability to deal with the problem of friend recommendation in large-sale OSNs. Moreover, the application case demonstrates that it can significantly improve friend recommendation service in the real world OSN.

Network Embedding Using Semi-Supervised Kernel Nonnegative Matrix Factorization

“…It can be noticed that the time complexity of SSKNMF is O(tn 2 d), which restricts SSKNMF to be applied effectively in large-scale networks. In our future work, we will focus on how to improve the efficiency of SSKNMF, while the implementations on distributed computing frameworks (e.g., Spark) will be carried out similarly as what we have made before in our previous work [41], [42]. Besides, to further explore the kernel of SSKNMF, we will also conduct more comparative experiments by using different kernel functions.…”

A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

“…Representative methods Topology networks NMTF [21], BNMTF [22], PCSNMF [23], PSSNMF [24], HPNMF [25], HNMF [26], A 2 NMF [27], PNMF [28] Signed networks JNMF [31], SGNMF [32], MCNMF [33], ReS-NMF [36], BRSNMF [37], SPOCD [38] Attributed networks FSL [40], JWNMF [41], NMTFR [42], CFOND [43], SCI [44], ASCD [45], DII [46], RSECD [47] Multi-layer networks WSSNMTF [50], NF-CCE [51], MTRD [53], LJ-SNMF [54], S2-jNMF [55] Dynamic networks sE-NMF [57], GrENMF [58], Cr-ENMF [59], ECGNMF [60], DGR-SNMF [61], DBNMF [62], C 3 [66], Chimera [70] Large-scale networks BIGCLAM [73], HierSymNMF2 [75], cyclicCDSymNMF [77], OGNMF [79], DRNMFSR [80], TCB [81] are often utilized. One is prior knowledge, also known as semi-supervised information, such as ground-truth community labels, node must-link and cannot-link constraints.…”

A learning to rank framework for future friendship ranking in streaming heterogeneous social networks

“…This computes the weight of the link, Friend → @M as elaborated in Section 3.2.1. For each user pair associated with a hashtag, the hashtag count for the same is incremented by 1 x , which computes the weight of the link, Friend → #tag, as described in Section 3.…”