Abstract
Social network analysis has recently attracted lots of attention among researchers due to its wide applicability in capturing social interactions. Link prediction, related to the likelihood of having a link between two nodes of the network that are not connected, is a key problem in social network analysis. Many methods have been proposed to solve the problem. Among these methods, similarity-based methods exhibit good efficiency by considering the network structure and using as a fundamental criterion the number of common neighbours between two nodes to establish structural similarity. High structural similarity may suggest that a link between two nodes is likely to appear. However, as shown in the paper, the number of common neighbours may not be always sufficient to provide comprehensive information about structural similarity between a pair of nodes. To address this, a neighbourhood vector is first specified for each node. Then, a novel measure is proposed to determine the similarity of each pair of nodes based on the number of common neighbours and correlation between the neighbourhood vectors of the nodes Experimental results, on a range of different real-world networks, suggest that the proposed method results in higher accuracy than other state-of-the-art similarity-based methods for link prediction.
Highlights
In order to evaluate the performance of the proposed Direct-Indirect Common Neighbours (DICN) method, this method and another 8 representative methods from the literature were implemented in Java and executed on a PC with an i5 2.3 GHz processor and 8 MB memory
The eight methods used for comparison are: Common Neighbours (CN)[8], Preferential Attachment Index (PA)[10], Jaccard Index (JC)[11], Hub Promoted Index (HPI)[28], Common Neighbours Degree Penalization (CNDP)[15], Node-coupling Clustering (NCC)[17], Parameterized Algorithm (CCPA)[16] and Significance of Higher-Order Path Index (SHOPI)[29]
Four different experiments are performed. Their objective is, respectively, to: (1) assess the accuracy of DICN when compared to other methods; (2) assess the robustness of DICN, with different sizes of training data; (3) and (4) validate Hypothesis 1 and 2 described earlier in the motivating section
Summary
As suggested by Ke-ke et al.[18], the number of common neighbours between a pair of nodes reveals structural similarity between the nodes and has a straight relationship with the link between the pair. Of neighbours is essentially split in half This is another suggestion that the number of common neighbours may not be a good indicator for link prediction. For instance in the network shown, nodes 4 and 5 have no common neighbours, but the correlation between their neighbours, i.e., nodes 2 and 3, may reveal a latent relationship between the two nodes, which correlates with the possibility of a future link between them. This kind of latent relationship should be considered for link prediction. Hypothesis 2: Considering latent relationships helps justify differences in existing and non-existing links between pairs of nodes that may still have the same number of common neighbours
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
