An Zeng

Ranking spreaders by decomposing complex networks

Abstract Ranking the nodesʼ ability of spreading in networks is crucial for designing efficient strategies to hinder spreading in the case of diseases or accelerate spreading in the case of information dissemination. In the well-known k-shell method, nodes are ranked only according to the links between the remaining nodes (residual links) while the links connecting to the removed nodes (exhausted links) are entirely ignored. In this Letter, we propose a mixed degree decomposition (MDD) procedure in which both the residual degree and the exhausted degree are considered. By simulating the epidemic spreading process on real networks, we show that the MDD method can outperform the k-shell and degree methods in ranking spreaders.

Extracting the information backbone in online system

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such “less can be more” feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency.

Prediction in complex systems: the case of the international trade network

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.

Network-based recommendation algorithms: A review

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users’ past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use–such as the possible influence of recommendation on the evolution of systems that use it–and finally discuss open research directions and challenges.

TREND PREDICTION IN TEMPORAL BIPARTITE NETWORKS: THE CASE OF MOVIELENS, NETFLIX, AND DIGG

Online systems, where users purchase or collect items of some kind, can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.

Recovery of infrastructure networks after localised attacks

The stability of infrastructure network is always a critical issue studied by researchers in different fields. A lot of works have been devoted to reveal the robustness of the infrastructure networks against random and malicious attacks. However, real attack scenarios such as earthquakes and typhoons are instead localised attacks which are investigated only recently. Unlike previous studies, we examine in this paper the resilience of infrastructure networks by focusing on the recovery process from localised attacks. We introduce various preferential repair strategies and found that they facilitate and improve network recovery compared to that of random repairs, especially when population size is uneven at different locations. Moreover, our strategic repair methods show similar effectiveness as the greedy repair. The validations are conducted on simulated networks, and on real networks with real disasters. Our method is meaningful in practice as it can largely enhance network resilience and contribute to network risk reduction.

Measuring the robustness of link prediction algorithms under noisy environment

Link prediction in complex networks is to estimate the likelihood of two nodes to interact with each other in the future. As this problem has applications in a large number of real systems, many link prediction methods have been proposed. However, the validation of these methods is so far mainly conducted in the assumed noise-free networks. Therefore, we still miss a clear understanding of how the prediction results would be affected if the observed network data is no longer accurate. In this paper, we comprehensively study the robustness of the existing link prediction algorithms in the real networks where some links are missing, fake or swapped with other links. We find that missing links are more destructive than fake and swapped links for prediction accuracy. An index is proposed to quantify the robustness of the link prediction methods. Among the twenty-two studied link prediction methods, we find that though some methods have low prediction accuracy, they tend to perform reliably in the “noisy” environment.

Ranking scientific publications with similarity-preferential mechanism

Along with the advance of internet and fast updating of information, nowadays it is much easier to search and acquire scientific publications. To identify the high quality articles from the paper ocean, many ranking algorithms have been proposed. One of these methods is the famous PageRank algorithm which was originally designed to rank web pages in online systems. In this paper, we introduce a preferential mechanism to the PageRank algorithm when aggregating resource from different nodes to enhance the effect of similar nodes. The validation of the new method is performed on the data of American Physical Society journals. The results indicate that the similarity-preferential mechanism improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Though our method is only applied to citation networks in this paper, it can be naturally used in many other real systems, such as designing search engines in the World Wide Web and revealing the leaderships in social networks.

Reconstructing propagation networks with temporal similarity

Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.

Temporal effects in trend prediction: identifying the most popular nodes in the future.

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.