Link Prediction in Weighted Networks: A Weighted Mutual Information Model

Zhu, Boyao; Xia, Yongxiang

doi:10.1371/journal.pone.0148265

Cited by 51 publications

(29 citation statements)

References 29 publications

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“…Accordingly, a number of methods are recently proposed to predict missing links in weighted networks [34][35][36][37][38][39] . LO can be directly extended to weighted networks via replacing the adjacency matrix A by the weight matrix W, where w ij denotes link weight between nodes i and j and w ij = 0 if i and j are disconnected.…”

Section: * Weighted Networkmentioning

confidence: 99%

Link prediction via linear optimization

Pech

Dong

Lee

et al. 2019

Physica A: Statistical Mechanics and its Applications

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Link prediction is an elemental challenge in network science, which has already found applications in guiding laboratorial experiments, digging out drug targets, recommending online friends, probing network evolution mechanisms, and so on. With a simple assumption that the likelihood of the existence of a link between two nodes can be unfolded by a linear summation of neighboring nodes' contributions, we obtain the analytical solution of the optimal likelihood matrix, which shows remarkably better performance in predicting missing links than the state-of-the-art algorithms for not only simple networks, but also weighted and directed networks. To our surprise, even some degenerated local similarity indices from the solution outperform well-known local indices, which largely refines our knowledge, for example, the number of 3-hop paths between two nodes more accurately predicts missing links than the number of 2-hop paths (i.e., the number of common neighbors), while in previous methods, longer paths are always considered to be less important than shorter paths.Thanks to the breakthrough in uncovering the structural complexity (e.g., small-world 1 and scale-free 2 properties) in real networks, the recent twenty years have witnessed an explosion in the studies of networks, which is turning the so-called network science from niche branches of science in mathematics (i.e., graph theory) and social science (i.e., social network analysis) to an interdisciplinary focus that attracts increasing attentions from physicists, mathematicians, social scientists, computer scientists, biologists, and so on. Recently, the research focus of network science has been shifting from macroscopic statistical regularities 3 to different roles played by microscopic elements, such as nodes 4 and links 5 , in network structure and functions. Therein, link prediction is an elemental challenge that aims at estimating the likelihood that a nonobserved link exists, on the basis of observed links in a network 6 .Link prediction is of particular significance. Theoretically speaking, link prediction can be used as a probe to quantify to which extent the network formation and evolution can be explained by a mechanism model, since a better model should be in principle transferred to a more accurate algorithm 7, 8 . Beyond theoretical interests, link prediction has already found many applications.For example, our knowledge of biological interactions is highly limited, with approximately 99.7% of the molecular interactions in human beings still unknown 9 . Instead of blindly checking all possible interactions, to predict based on known interactions and focus on those links most likely to exist can sharply reduce the experimental costs if the predictions are accurate enough 10 . Analogously, the known interactions between drugs and target proteins are very limited, while it is believed that any single drug can interact with multiple targets 11 . By this time, link prediction algorithms have already played a critical role in finding out new uses of old d...

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Section: * Weighted Networkmentioning

confidence: 99%

Link prediction via linear optimization

Pech

Dong

Lee

et al. 2019

Physica A: Statistical Mechanics and its Applications

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“…20 structural information, including common neighbors, were employed to facilitate the link prediction task. The extension of this work has been employed in a weighted complex network21. These entropy-based similarity indices have been evaluated over a number of complex networks and compared to common proximity measures.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, information theory has been employed for link prediction problem in homogeneous complex networks192021. The main contribution of these works is to measure the information provided by common topological features, such as common neighbors, instead of using them as simple topological features.…”

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confidence: 99%

Mutual information model for link prediction in heterogeneous complex networks

Shakibian

Charkari

2017

Sci Rep

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Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.

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“…First, some links may be missing from the data, in which case we need to predict these missing links from the available data. This link prediction problem has received much attention in the past decade89101112, and many link prediction algorithms have been proposed for both unweighted91314151617181920212223242526 and weighted2728293031 networks. Second, the weights of some links may be unavailable.…”

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confidence: 99%

Weight prediction in complex networks based on neighbor set

Zhu

Xia

Zhang

2016

Sci Rep

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Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases.

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Link Prediction in Weighted Networks: A Weighted Mutual Information Model

Cited by 51 publications

References 29 publications

Link prediction via linear optimization

Link prediction via linear optimization

Mutual information model for link prediction in heterogeneous complex networks

Weight prediction in complex networks based on neighbor set

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