Measuring the influence of scientists and their activities on science and society is important and indeed essential for many studies. Despite the substantial efforts devoted to exploring the influence’s measures and patterns of an individual scientific enterprise, it remains unclear how to quantify the mutual impact of multiple scientific activities. This work quantifies the relationship between the scientists’ interactive activities and their influences with different patterns in the AMiner dataset. Specifically, inflation treatment and field normalization are introduced to process the big data of paper citations as the scientist’s influence, and then the evolution of the influence is investigated for scientific activities in the citation and cooperation patterns through the Hawkes process. The results show that elite scientists have higher individual and interaction influences than ordinary scientists in all patterns found in the study, with permutation tests verifying the significance of the new findings. Moreover, the study compares the patterns found in two largest disciplines, i.e.,STEMandHumanities, revealing the higher value of individual influence inSTEMthan inHumanities. Furthermore, it is found that the opposite trend ofSTEMandHumanitiesin the cooperation pattern suggests different cooperation habits of scientists in different disciplines. Overall, this investigation provides a feasible approach to addressing the scientific influence issue and deepening the quantitative understanding of the mutual influence of multiple scientific activities in science and society.

Critical node detection (CND) is commonly used to detect nodes with a high impact on network robustness. It has been widely used in disease propagation, social networks, communications, and other fields. As a nondeterministic polynomial-time (NP)-complete problem, the efficiency of solving CND severely limits the scale of the available network. Fortunately, the evolutionary algorithm (EA) is an effective method to solve this problem. However, although EA improves the global search capability of the algorithm by preserving gene diversity, it also introduces many inferior genes, thus expanding the candidate solution space, reducing the search efficiency, and making it difficult to apply the pruning algorithm directly to its solution space. Hence, indirectly reducing the solution space of EA by deleting inferior genes is a feasible pruning method; however, the interaction of multiple genes affects the quality of CND solutions, making it a challenge to pick out inferior individual genes. Therefore, this work proposes a multipopulation synergistic gene screening algorithm based on the parallelism of EA and combined with Ensemble learning for identifying low-quality genes and removing them as a way of pruning the solution space of the algorithm and improving the search efficiency. The algorithm encodes all nodes in the graph as the gene pool of EA and treats a single population as a weak learner to screen the dominant genes in the gene pool and achieve fast pruning of EA’s solution space by integrating the dominant individuals in multiple populations. In this work, the experiments demonstrate the effectiveness of the proposed method and analyze the effect of different network structures on the algorithm.

Critical nodes detection (CND) focuses on identifying the nodes that significantly impact the network’s robustness and is applied in various fields such as power grids, communication networks, and disease spreading. However, detecting the critical nodes is a challenging nondeterministic polynomial time complete (NP-complete) problem. One possible solution is using the evolutionary algorithm which has a high global search capability. However, the existing evolutionary algorithms for CND only focus on independent nodes, ignoring the underlying relationship among the nodes. Thus, in this work, we proposed a new topology-combined differential evolution framework called TDE to explore the possibility of improving the performance by fusing topology information, which designs individual genotypes through node degree, and new mutation and decoding-based selection operators are designed for these genotypes to use topology information effectively. The experiments on synthetic and real networks show that it is feasible to improve the search capability of the algorithm by fusing node degree information.

Location-Based Social Networks have been widely studied in recent years; new approaches constantly developed to solve individuals’ trajectory prediction tasks. However, most of these methods require sufficient data to learn individual features, which is not always satisfied in real situations, especially for online data. The digital data on human behavior typically follows a power-law distribution, indicating that only a few people have rich activities recorded while most people’s behavioral data are limited. In order to overcome this hurdle, our work constructs the user behavior proximity network (UBPN) and proposes a new walking strategy based on this network that extracts the hidden information from the social contacts to substitute the unobserved behavioral information of an individual. Specifically, our proposed walking strategy has two walking paths, accounting for the temporal and social information on the ego users’ and their alters’ mobility activities. This walking strategy is model-agnostic and can be integrated with many existing walk-based deep learning methods. Our work applies the methods on two real-world datasets with rich spatiotemporal information and shows that the performances of the existing prediction methods improve significantly by integrating the proposed walking strategy.

Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this article, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.

In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction method named Resource Allocation Index (RA) is adopted for privacy attacks. Several defense methods are proposed, including heuristic and evolutionary approaches, to protect targeted links from RA attack. In particular, incremental computation is proposed for accelerating the calculation of fitness in evolutionary approaches. This is the first time to study privacy protection for targeted links against similarity based link prediction attacks. Some links are randomly selected from original network as targeted links for experimentation. The experimental results on nine real-world networks demonstrate the superiority of the evolutionary perturbations, especially EDA, for defending against RA attack. Moreover, experimental results show that the proposed perturbation generated by EDA is transferable and can even defend against other link prediction attacks which are based on high order similarity between pairwise nodes, although it is designed to prevent RA attack.

Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link prediction of static network, while real-world networks always evolve over time with the occurrence and vanishing of nodes and links. Dynamic network link prediction (DNLP) thus has been attracting more and more attention since it can better capture the evolution nature of networks, but still most algorithms fail to achieve satisfied prediction accuracy. Motivated by the excellent performance of long short-term memory (LSTM) in processing time series, in this article, we propose a novel encoder-LSTM-decoder (E-LSTM-D) deep learning model to predict dynamic links end to end. It could handle long-term prediction problems, and suits the networks of different scales with fine-tuned structure. To the best of our knowledge, it is the first time that LSTM, together with an encoder-decoder architecture, is applied to link prediction in dynamic networks. This new model is able to automatically learn structural and temporal features in a unified framework, which can predict the links that never appear in the network before. The extensive experiments show that our E-LSTM-D model significantly outperforms newly proposed DNLP methods and obtain the state-of-the-art results.

Open source software (OSS) projects and communities are becoming increasingly popular and influential recently. Communications and collaborations are essential for the success of projects. Usually, the most active and productive programmers are awarded with promotion to developers. To more effectively manage and progress the projects, it is important and beneficial to rank the programmers and thus, predict the developer candidates. In this work, we propose to combine machine learning techniques with existing complex network node ranking algorithms to improve the prediction results. Specifically, we have made the following contributions: (1), we have designed a novel machine learning-based classifier with significantly improved prediction performance; (2), we have constructed and tested various networks built based on the programmer email communication information; and (3), we have used real-world project data to compare different techniques and validate our methods. Experimental results demonstrate that our technique reduces the error rate by 25% compared with the second best. Moreover, we discover that the [Formula: see text] nearest neighbor (KNN)-based machine learning algorithm and non-directional temporal network with a time window of 1–3 months give the best prediction results.

Animals search for food based on certain optimal principles and over time form foraging patterns effective for survival in changing environments. Due to the many choices available in modern society, we also face a decision on where to get their food. We call this “modern human food foraging,” since the Internet makes foraging much more convenient than before. People search online for food venues, or restaurants, through websites such as Yelp, and write reviews for the food they tasted, which in turn, facilitate others' searches in the future. These activities make the whole community of restaurant patrons wiser over time. Moreover, the archives of all these choices and evaluations are publicly available, and can help researchers better understand human foraging patterns in modern society. In this paper, we use a Yelp data set to study modern human food foraging patterns, with respect to both geography and cuisine. To understand spatial patterns, we cluster reviewed restaurants geographically and construct a taste similarity network, representing the topology of restaurant cuisine space. We find that people steadily expand their foraging domains from the nearest to them to the distant in geography and from the most familiar to the novel in cuisine. Using longitudinal data of restaurant reviews, we build a geographical foraging network and a taste foraging network for each patron based on which, we propose three kinds of entropies to characterize foraging patterns. We show that the modern foraging patterns of restaurant patrons in both geography and cuisine are of high regularity, indicating that their behaviors are rather predictable. The foraging patterns are also associated with individual social status in the community. Namely, people having a higher variety in the restaurant cuisines they have visited, but fewer actual locations they visited, tend to attract more followers.

Social synchrony (SS) is an emergent phenomenon in human society. People often mimic others which, over time, can result in large groups behaving similarly. Drawing from prior empirical studies of SS in online communities, here we propose a discrete network model of SS based on four attributes: 1) depth of action; 2) breadth of impact, i.e., a large number of actions are performed with a large group of people involved; 3) heterogeneity of role, i.e., people of higher degree play more important roles; and 4) lastly, emergence of phenomenon, i.e., it is far from random. We analyze our model both analytically and with simulations, and find good agreement between the two. We find this model can well explain the four characters of SS, and thus hope it can help researchers better understand human collective behavior.

Real-world networks feature weights of interactions, where link weights often represent some physical attributes. In many situations, to recover the missing data or predict the network evolution, we need to predict link weights in a network. In this paper, we first proposed a series of new centrality indices for links in line graph. Then, utilizing these line graph indices, as well as a number of original graph indices, we designed three supervised learning methods to realize link weight prediction both in the networks of single layer and multiple layers, which perform much better than several recently proposed baseline methods. We found that the resource allocation index (RA) plays a more important role in the weight prediction than other topological properties, and the line graph indices are at least as important as the original graph indices in link weight prediction. In particular, the success application of our methods on Yelp layered network suggests that we can indeed predict the offline co-foraging behaviors of users just based on their online social interactions, which may open a new direction for link weight prediction algorithms, and meanwhile provide insights to design better restaurant recommendation systems.

Passive indoor localization is important. Unlike active localization techniques, it does not require for users to carry measuring devices, e.g., smart phones. Thus, it is widely used in applications such as security, smart housing, object tracking, etc. However, in real-world applications, the passive localization accuracy is limited due to the environment noises, multipath effect, etc. To address those problems, in this paper, we propose to use channel state information (CSI) instead. Specifically, we make the following contributions: 1) we design a CSI-based passive indoor localization system; 2) we develop a Naive Bayes classifier enhanced with confidence level information; and 3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve more than 86% accuracy on average and at least 15% better than the baseline Naive Bayes classifier.