A novel similarity measure for mining missing links in long-path networks

Ran, Yijun; Li, Tianyu; Jia, Tao; Xu, Xiao-Ke

doi:10.1088/1674-1056/ac4483

Cited by 6 publications

(14 citation statements)

References 66 publications

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Section: Introductionmentioning

confidence: 61%

“…In this study, we extend the structural equivalence and shortest path length hypotheses to predict future links between new nodes and old nodes in long-path networks with time-evolving [36]. This work generalizes our previous work [36] for an extension of long-path static networks into long-path networks with time-evolving. To solve the temporal link prediction with new nodes, here we integrate the network structure and external information (e.g., nodal attribute) to predict future links.…”

Section: Introductionmentioning

confidence: 61%

“…(4) SESPL Our previous work proposed a novel similarity measure, SESPL, which is associated with the principles of structural equivalence and shortest path length [36]. The experimental results showed that the SESPL predictor works better than other classical similarity-based predictors in long-path networks.…”

Section: Static Similarity Methodsmentioning

confidence: 99%

“…For example, we denote Funding0809 in table 1 as the network which is constructed by using the data of year 2008 and 2009, and then we study the likelihood of the emergence of links within year 2010 on Funding0809 network. According to our precious study [36], the 7 academic funding networks are all long-path networks, such as Funding0809 network shown in figure 2(b). More complete statistics of the funding dataset are given in table 1.…”

Section: Data Description and Processing 221 Data Descriptionmentioning

confidence: 99%

“…We define d as the average shortest path length of giant component in network G t . According this study, the networks with d < 9 are short-path networks, and the networks with d 9 are so-called long-path networks[36]. M t+1 represents the number of links in the future links set L P t+1 .…”

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confidence: 99%

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Predicting future links with new nodes in temporal academic networks

Ran

Liu

et al. 2022

J. Phys. Complex.

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Most real-world systems evolve over time in which entities and the interactions between entities are added and removed---new entities or relationships appear and old entities or relationships vanish. While most network evolutionary models can provide an iterative process for constructing global properties, they cannot capture the evolutionary mechanisms of real systems. Link prediction is hence proposed to predict future links which also can help us understand the evolution law of real systems. The aim of link prediction is to uncover missing links from known parts of the network or quantify the likelihood of the emergence of future links from current structures of the network. However, almost all existing studies ignored that old nodes tend to disappear and new nodes appear over time in real networks, especially in social networks. It is more challenging for link prediction since the new nodes do not have pre-existing structure information. To solve the temporal link prediction problems with new nodes, here we take into account nodal Attribute Similarity and the Shortest Path Length, namely, $ASSPL$, to predict future links with new nodes. The results tested on scholar social network and academic funding networks show that it is highly effective and applicable for $ASSPL$ in funding networks with time-evolving. Meanwhile, we make full use of an efficient parameter to exploit how network structure or nodal attribute has an impact on the performance of temporal link prediction. Finally, we find that nodal attributes and network structure complement each other well for predicting future links with new nodes in funding networks.

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Section: Introductionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 61%

Section: Static Similarity Methodsmentioning

confidence: 99%

Section: Data Description and Processing 221 Data Descriptionmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting future links with new nodes in temporal academic networks

Ran

Liu

et al. 2022

J. Phys. Complex.

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A Weibo local network growth model constructed from the perspective of following–followed

Nian 年,

Yao 姚

2024

Chinese Phys. B

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In order to explore the evolution process of the Weibo local network, this study first defines four factors influencing the evolution of the Weibo network. On this basis, the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality, resulting in a Weibo local network evolution model based on user attributes and behavioral similarity. The model's validity was validated by comparing simulation results with real data. The findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive features. The results show that the Weibo local network exhibits both small-world characteristics and distinctive properties. The in-degree distribution follows a mixed pattern of exponential and power-law distributions, the degree-degree shows isomatching, and both the in-degree centrality and eigenvector centrality values are relatively low. This research contributes to our understanding of user behaviour in the Weibo network, and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.

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The maximum capability of a topological feature in link prediction

Ran,

Xu,

Jia

2024

PNAS Nexus

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Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature’s capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.

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A novel similarity measure for mining missing links in long-path networks

Cited by 6 publications

References 66 publications

Predicting future links with new nodes in temporal academic networks

Predicting future links with new nodes in temporal academic networks

A Weibo local network growth model constructed from the perspective of following–followed

The maximum capability of a topological feature in link prediction

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