Predicting missing links via correlation between nodes

Liao, Hao; Zeng, An; Zhang, Yicheng

doi:10.1016/j.physa.2015.05.009

Cited by 39 publications

(19 citation statements)

References 44 publications

(42 reference statements)

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“…degree, clustering), the hybrid-based methods have been studied. Liao et al 28 presented a novel correlation-based index and combined it with the AA index to achieve better performance. Zeng 29 presented a hybrid index through combining the CN index with the preferential attachment index (PA).…”

Section: Related Workmentioning

confidence: 99%

“…In Ref. 28, the feature of one node i was characterized as an attribute vector v i derived from the adjacency matrix A of network graph, where v i ¼ ða i1 ; a i2 ; . .…”

Section: Relevance Between Layers In Multiplex Networkmentioning

confidence: 99%

“…Generally, it is di±cult to obtain su±ciently available topology information for new or small degree nodes, which is the key di±culty of the cold-start problem. 28 The multiplex networks have natural advantages in topology information because they characterize the relationship of individuals more diverse than the single-layer networks. Therefore, one can predict the behavior of new or small degree individuals based on their background relationships or behaviors.…”

Section: The In°uence Of Tunable Parameter On Prediction Performancementioning

confidence: 99%

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Link prediction via layer relevance of multiplex networks

Yao

Zhang

Yang

et al. 2017

Int. J. Mod. Phys. C

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In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can a®ect that of other layers in multiplex networks. In this paper, the e®ect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel \Node Similarity Index" based on \Layer Relevance" (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can signi¯cantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

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Section: Related Workmentioning

confidence: 99%

“…In Ref. 28, the feature of one node i was characterized as an attribute vector v i derived from the adjacency matrix A of network graph, where v i ¼ ða i1 ; a i2 ; . .…”

Section: Relevance Between Layers In Multiplex Networkmentioning

confidence: 99%

Section: The In°uence Of Tunable Parameter On Prediction Performancementioning

confidence: 99%

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Link prediction via layer relevance of multiplex networks

Yao

Zhang

Yang

et al. 2017

Int. J. Mod. Phys. C

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“…All non-observed links are ranked according to their similarities, and the non-observed links connecting nodes that are more similar are supposed to have higher existence likelihoods. Node similarity can be defined by using the essential attributes of nodes: two nodes are considered similar if they have many common features or correlated topological structures [1,15,26]. Many studies found that there are substantial levels of topical similarity among individuals who are close to one another in the social network.…”

Section: Related Workmentioning

confidence: 99%

A fast algorithm for predicting links to nodes of interest

Chen

2016

Information Sciences

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The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can.

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“…An alternative classification distinguishes between algorithms employing purely structural information (either binary or weighted [8]) and algorithms making use of some kind of external information as well (e.g. nodes attributes [22]).…”

Section: Introductionmentioning

confidence: 99%

Entropy-based approach to missing-links prediction

2018

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Link-prediction is an active research field within network theory, aiming at uncovering missing connections or predicting the emergence of future relationships from the observed network structure. This paper represents our contribution to the stream of research concerning missing links prediction. Here, we propose an entropy-based method to predict a given percentage of missing links, by identifying them with the most probable non-observed ones. The probability coefficients are computed by solving opportunely defined null-models over the accessible network structure. Upon comparing our likelihood-based, local method with the most popular algorithms over a set of economic, financial and food networks, we find ours to perform best, as pointed out by a number of statistical indicators (e.g. the precision, the area under the ROC curve, etc.). Moreover, the entropy-based formalism adopted in the present paper allows us to straightforwardly extend the link-prediction exercise to directed networks as well, thus overcoming one of the main limitations of current algorithms. The higher accuracy achievable by employing these methods -together with their larger flexibilitymakes them strong competitors of available link-prediction algorithms.

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Predicting missing links via correlation between nodes

Cited by 39 publications

References 44 publications

Link prediction via layer relevance of multiplex networks

Link prediction via layer relevance of multiplex networks

A fast algorithm for predicting links to nodes of interest

Entropy-based approach to missing-links prediction

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