Predicting top-L missing links with node and link clustering information in large-scale networks

Robust link prediction in criminal networks: A case study of the Sicilian Mafia

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Link prediction aims to identify missing links within static networks or estimate the probability of emerging links in dynamic networks, representing a critical and challenging research direction in complex network. Many similarity-based methods have been established from various viewpoints, however, there are relatively few methods that take into account both the path information between node pairs and the nodes along the path. To fill this gap, we propose two novel link prediction algorithms, namely LPRA and LPH. The core idea is that the similarity between node pairs is closely related to the local paths connecting the two nodes and the resource transition capability of the nodes along those paths. The algorithms utilize the local paths with adjustable lengths between node pairs and the topological information of the nodes on the path to calculate the similarity index, which incorporate the resource transition capabilities of all the nodes on the possible paths between node pairs. We conducted multiple groups of comparative experiments on 10 real-world networks to validate the effectiveness of the proposed algorithm. Experimental results demonstrate that LPRA and LPH outperform nine classical methods and five recently popular methods. Moreover, LPRA demonstrates a significant difference level (P-value [Formula: see text]) compared to most of the methods in the ANOVA of AUC accuracy, indicating a statistically significant performance advantage of LPRA.

Link prediction is usually studied in unweighted networks, while ignoring the importance of weights in complex networks. In this paper, an algorithm for link prediction with link weight assignments is proposed for unweighted networks. First, weights of links are assigned based on the node degree and clustering coefficient, which describe the closeness of two nodes in complex networks; second, the weighted form of similarity index instead of the unweighted form are applied for link prediction; finally, we compare the performance of link predicitions in networks with or without weight assignments. Experiments on four commonly used complex network data sets have been conducted, and the results show that weights of links play an important role in the complex networks, and by assigning weights to links accordingly the algorithm proposed can improve the link prediction performance.

Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms all operate under the assumption that networks are complete. Thus, their performance is vulnerable to missing data and suffers from poor scalability. In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates a probabilistic learning framework that separately models pairs of node-context and node-attribute relationships. Different from existing attributed network embedding algorithms, SINE provides greater flexibility to make the best of useful information and mitigate negative effects of missing information on representation learning. A stochastic gradient descent based online algorithm is derived to learn node representations, allowing SINE to scale up to large-scale networks with high learning efficiency. We evaluate the effectiveness and efficiency of SINE through extensive experiments on real-world networks. Experimental results confirm that SINE outperforms state-of-the-art baselines in various tasks, including node classification, node clustering, and link prediction, under settings with missing links and node attributes. SINE is also shown to be scalable and efficient on large-scale networks with millions of nodes/edges and high-dimensional node features. The source code of this paper is available at https://github.com/daokunzhang/SINE.

Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms can be applied, being less or more effective in the relevant scenarios. In this work, we develop a novel framework that attempts to compose the best features of link prediction algorithms when applied to a network, in order to have even more reliable predictions, especially in topologies emerging in Industrial Internet of Things (IIoT) environments. According to the proposed framework, we first apply appropriate link prediction algorithms that we have chosen for an analyzed network (basic algorithms). Each basic algorithm gives us a numerical estimate for each missing edge in the network. We store the results of each basic algorithm in appropriate structures. Then we provide them as input to a developed genetic algorithm. The genetic algorithm evaluates the results of the basic algorithms for each missing edge of the network. At each missing edge of the network and from generation to generation, it composes the estimates of the basic algorithms regarding each edge and produces a new optimized estimate. This optimization results in a vector of weights where each weight corresponds to the effectiveness of the prediction for each of the basic algorithms we have employed. With these weights, we build a new enhanced predictor tool, which can obtain new optimized estimates for each missing edge in the network. The enhanced predictor tool applies to each missing edge the basic algorithms, normalizes the basic algorithms’ estimates, and, using the weights of the estimates derived from the genetic algorithm, returns a new estimate of whether or not an edge will be added in the future. According to the results of our experiments on several types of networks with five well-known link prediction algorithms, we show that the new enhanced predictor tool yields in every case better predictions than each individual algorithm, therefore providing an accuracy-targeting alternative in the existing state of the art.

BackgroundPredicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine.ResultsIn this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant.ConclusionsWe propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Link prediction is one of the most important and challenging tasks in complex network analysis, which aims to predict the existence of unknown links based on the known information in the network. As critical topological properties in the network, node’s degree and clustering coefficient are well-suited for describing the tightness of connection between nodes. The importance of node can affect the possibility of link existence to a certain extent. By analyzing the impact of different centrality on links, which concluded that the degree centrality and proximity centrality have the greatest influence on network link prediction. A link prediction algorithm combines importance of node and network topological properties, called DCCLP, is proposed in this paper, the symmetry of the adjacency matrix is considered in the DCCLP link prediction algorithm to further describe the structural similarity of network nodes. In the training phase of the DCCLP algorithm, the maximized AUC indicator in the training set as the objective, and the optimal parameters are estimated by utilizing the White Shark Optimization algorithm. Then the prediction accuracy of the DCCLP algorithm is evaluated in the test set. By experimenting on twenty-one networks with different scales, and comparing with existing algorithms, the experimental results show that the effectiveness and feasibility of DCCLP algorithm, and further illustrate the importance of the degree centrality of node pairs and proximity centrality of nodes to improve the prediction accuracy of link prediction.

Link prediction is a common problem in network science that cuts across many disciplines. The goal is to forecast the appearance of new links or to find links missing in the network. Typical methods for link prediction use the topology of the network to predict the most likely future or missing connections between a pair of nodes. However, network evolution is often mediated by higher-order structures involving more than pairs of nodes; for example, cliques on three nodes (also called triangles) are key to the structure of social networks, but the standard link prediction framework does not directly predict these structures. To address this gap, in recent work, we propose a new link prediction task called “pairwise link prediction” that directly targets the prediction of new triangles, where one is tasked with finding which nodes are most likely to form a triangle with a given edge. We extend this work in this manuscript, and we evaluate a variety of natural extensions to link prediction methods including neighborhood and PageRank-based methods. A key difference from our previous work is the definition of the neighborhood of an edge, which has a surprisingly large impact on the empirical performance. Our experiments on a variety of networks show that diffusion-based methods are less sensitive to the type of graphs used and more consistent in their results. We also show how our pairwise link prediction framework can be used to get better predictions within the context of standard link prediction evaluation.

Link prediction is a key tool for studying the structure and evolution mechanism of complex networks. Recommending new friend relationships through accurate link prediction is one of the important factors in the evolution, development, and popularization of social networks. At present, scholars have proposed many link prediction algorithms based on the similarity of local information and random walks. These algorithms help identify actual missing and false links in various networks. However, the prediction results significantly differ in networks with various structures, and the prediction accuracy is low. This study proposes a method for improving the accuracy of link prediction. Before link prediction, k‐shell decomposition method is used to layer the network, and the nodes that are in 1‐shell and the nodes that are not linked to the high‐shell in the 2‐shell are deleted. The experiments on four real network datasets verify the effectiveness of the proposed method.

Optimal pruned tree-cut mapping-based fast shielding for large-scale networks

Link prediction is one of the fundamental problems for graph-structured data. However, a number of applications of link prediction, such as predicting commercial ties or memberships within a criminal organization, are adversarial, with another party aiming to minimize its effectiveness by manipulating observed information about the graph. In this paper, we focus on the feasibility of mounting adversarial attacks against link prediction algorithms based on graph neural networks. We first propose a greedy heuristic that exploits incremental computation to find attacks against a state-of-the-art link prediction algorithm, called SEAL. We then design an efficient variant of this algorithm that incorporates the link formation mechanism and Υ-decaying heuristic theory to design more effective adversarial attacks. We used real-world datasets and performed an extensive array of experiments to show that the performance of SEAL is negatively affected by a significant margin. More importantly, our experimental results have shown that our adversarial attacks mounted based on SEAL can be readily transferred to several existing link prediction heuristics in the literature.

Link prediction algorithms in complex networks, such as social networks, biological networks, drug-drug interactions, communication networks, and so on, assign scores to predict potential links between two nodes. Link prediction (LP) enables researchers to learn unknown, new as well as future interactions among the entities being modeled in the complex networks. In addition to measures like degree distribution, clustering coefficient, centrality, etc., another metric to characterize structural properties is network assortativity which measures the tendency of nodes to connect with similar nodes. In this paper, we explore metrics that effectively predict the links based on the assortativity profiles of the complex networks. To this end, we first propose an approach that generates networks of varying assortativity levels and utilize three sets of link prediction models combining the similarity of neighborhoods and preferential attachment. We carry out experiments to study the LP accuracy (measured in terms of area under the precision-recall curve) of the link predictors individually and in combination with other baseline measures. Our analysis shows that link prediction models that explore a large neighborhood around nodes of interest, such as CH2-L2 and CH2-L3, perform consistently for assortative as well as disassortative networks. While common neighbor-based local measures are effective for assortative networks, our proposed combination of common neighbors with node degree is a good choice for the LP metric in disassortative networks. We discuss how this analysis helps achieve the best-parameterized combination of link prediction models and its significance in the context of link prediction from incomplete social and biological network data.

Link prediction is the problem of predicting the location of either unknown or fake links from uncertain structural information of a network. Link prediction algorithms are useful in gaining insight into different network structures from partial observations of exemplars. However, existing link prediction algorithms only focus on regular complex networks and are overly dependent on either the closed triangular structure of networks or the so-called preferential attachment phenomenon. The performance of these algorithms on highly sparse or treelike networks is poor. In this letter, we proposed a method that is based on the network heterogeneity. We test our algorithms for three real large sparse networks: a metropolitan water distribution network, a Twitter network, and a sexual contact network. We find that our method is effective and performs better than traditional algorithms, especially for the Twitter network. We further argue that heterogeneity is the most obvious defining pattern for complex networks, while other statistical properties failed to be predicted. Moreover, preferential attachment based link prediction performed poorly and hence we infer that preferential attachment is not a plausible model for the genesis of many networks. We also suggest that heterogeneity is an important mechanism for online information propagation.

The target of link prediction is used to estimate the possibility of future links among nodes through known network structures and nodes information. According to the time-varying characteristics of the opportunistic network, the historical information of node pairs has a significant influence on the future connection state. We propose a novel link prediction approach which is based on the recurrent neural network link prediction (RNN-LP) framework. With the help of time series method, we define the vector that is made up of the node information and historical connection information of the node pairs, in which a sequence vector is constructed. Benefiting from RNN in sequence modeling, the time domain characteristics were extracted in the process of the dynamic evolution of the opportunistic network. Hence, the future link prediction becomes significantly better. By utilizing iMote traces Cambridge and MIT reality datasets, experimental results are obtained to reveal that RNN-LP method gives better accuracy and stability than the prediction techniques of the common neighbor, Adamic-Adar, resource allocation, local path, and Katz.