Local community detection aims to detect local communities that have expanded from the given node. Because of the convenience of obtaining the local information of the network and nearly linear time complexity, researchers have proposed many local community detection algorithms to discover the community structure of real-world networks and have obtained excellent results. Most existing local community detection algorithms expand from the given node to a community based on an expansion mechanism that can determine the membership of nodes. However, when determining the membership of neighboring nodes of a community, previous algorithms only considered the impact from the current community, but the impact from the potential communities around the node was neglected. As the name implies, a potential community is a community structure hidden in an unexplored network around a node. This paper gives the definition of potential communities of a node for the first time, that is, a series of connected components consisting of the node’s neighbors that are in the unexplored network. We propose a three-stage local expansion algorithm, named LCDPC , that performs Local Community Detection based on Potential Community exploration. First, we search for a suitable node to replace the given node as the seed by calculating the node importance and the node similarity. Second, we form the initial community by combining the seed and its suitable potential community. Finally, the eligible nodes are selected by comparing the similarities between potential communities and the expanding community and nodes and adding them to the initial community for community expansion. The proposed algorithm is compared with eight state-of-the-art algorithms on both real-world networks and artificial networks, and the experimental results show that the performance of the proposed algorithm is better than that of the comparison algorithms and that the application of potential community exploration can help identify the community structure of networks.

The problem of influence dissemination under competitive conditions can truly reflect the form of information in social networks, make reasonable decisions, and analyze the impact gain of different types of messages on users, so as to maximize the benefits of users. How to accurately simulate the spread of competitive influence is still a challenging problem. In such conditions to carry out the information dissemination, viral marketing, disease prevention and other works has a more reasonable significance. However, it is NP-hard to find the most influential node set in a competitive environment. Although an approximate optimal solution can be obtained by using a greedy algorithm to simulate influence propagation under competitive conditions, the time efficiency of the process is very low. Therefore, this paper designs a bionic optimized solution to explore the above problems. Firstly, We set optimization measures to balance the convergence and diversity of the optimization algorithm in the target space, and based on this, we propose a local impact ability evaluation metric based on the de-heterogeneous information within the 2-hop neighborhood of the node set to measure the propagation ability of the node set under competitive conditions. Secondly, the proposed evaluation index is applied to the improved particle swarm optimization algorithm to realize the screening of global optimal particles. Finally, we build a game model to simulate the interest-driven information ownership and dissemination of nodes. The effectiveness and efficiency of our method are verified by comparing the propagation range of competitive influence of different methods and the time complexity of each algorithm on real data sets.

Overlapping communities are ubiquitous in real-world systems. For overlapping community detection, local expansion methods excel in scalability and efficiency yet have poor tolerance to low-quality seeds and communities. Based on our previous work, we introduce a more robust local-expansion-based overlapping community detection algorithm, named CEO, performing Construction, Expansion and Optimization sub-processes. To solve the poor fault tolerance problem, CEO discards low-quality seeds and communities in each sub-process based on optimizing node memberships. CEO was compared to thirteen noted algorithms by examining the performance on five groups of artificial networks and sixteen real-world networks with ground-truth communities. Experimental results showed CEO performs the best in identifying overlapping communities, which verifies the effectiveness of discarding low-quality seeds and communities in solving the poor fault tolerance problem.

Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.

Aspect-based sentiment analysis is a classic fine-grained approach that aims to distinguish sentiment polarities towards a particular aspect target. The majority of research on this topic has been devoted to constructing syntax-based graph convolutional networks (GCNs) for context feature vectors. These approaches perform poorly in terms of node representation and capturing long-distance dependency. In this paper, we focus on the ability of graph convolution and propose an aggregated graph convolutional network (AGCN) to enhance the representation ability of target nodes. To exploit the node feature information, we introduce two aggregator functions to iteratively update the representation of each node from its local neighborhood. To extract more associated node information, we also apply the subdependency of nodes to aggregate the node features, and then employ the attention mechanism to capture the sentiment dependencies between different node feature information. The proposed AGCN is evaluated on large Chinese and English datasets to prove the effect of our model in aspect-based sentiment analysis. The experimental results show that our model is valid compared with other GCN-based methods.

With the prevalence of smartphones and the maturation of indoor positioning techniques, predicting the movement of a large number of customers in indoor environments has become a promising and challenging line of research in recent years. While most of the current predicting approaches that take advantage of mathematical methods perform well in outdoor settings, they exhibit poor performance in indoor environments. To solve this problem, in this study, a sequential similarity-based prediction approach which combines the spatial and semantic contexts into a unified framework is proposed. We first present a revised Longest Common Sub-Sequence (LCSS) algorithm to compute the spatial similarity of the indoor trajectories, and then a novel algorithm considering the indoor semantic R-tree is proposed to compute the semantic similarities; after this, a unified algorithm is considered to group the trajectories, and then the clustered trajectories are used to train the prediction models. Extensive performance evaluations were carried out on a real-world dataset collected from a large shopping mall to validate the performance of our proposed method. The results show that our approach markedly outperforms the baseline methods and can be used in real-world scenarios.

The influence maximization problem is designed to seek a set of nodes in a social network so that the set has the maximum information propagation capacity on the network. In response to the inefficiency of existing greedy algorithms and the low accuracy of centrality-based heuristics, we propose an improved differential evolution algorithm (IDDE) based on the network discretization in this paper. The algorithm improves the variance rule of the differential evolution algorithm, takes the discrete number and discrete granularity of the remaining network after the removal of the target node as the index to evaluate the importance of the node, and proposes a fitness function based on the robustness of the network. The method embodies symmetry in two aspects. Firstly, the global connectivity among nodes in the network decreases as the number of target nodes removed in the social network increases. Secondly, the gain of global influence range gradually becomes smaller as the number of target nodes screened by the proposed method increases. We conducted comparison experiments on four real datasets of different sizes, and the results show that the IDDE algorithm outperforms the comparison algorithm.

The connection between users in social networks can be maintained for a certain period of time, and the static network structure formed provides the basic conditions for various kinds of research, especially for discovering customer groups that can generate great influence, which is important for product promotion, epidemic prevention and control, and public opinion supervision, etc. However, the computational process of influence maximization ignores the timeliness of interaction behaviors among users, the screened target users cannot diffuse information well, and the time complexity of relying on greedy rules to handle the influence maximization problem is high. Therefore, this paper analyzes the influence of the interaction between nodes in dynamic social networks on information dissemination, extends the classical independent cascade model to a dynamic social network dissemination model based on effective links, and proposes a two-stage influence maximization solution algorithm (Outdegree Effective Link—OEL) based on node degree and effective links to enhance the efficiency of problem solving. In order to verify the effectiveness of the algorithm, five typical influence maximization methods are compared and analyzed on four real data sets. The results show that the OEL algorithm has good performance in propagation range and running time.

Unlike outdoor trajectory prediction that has been studied many years, predicting the movement of a large number of users in indoor space like shopping mall has just been a hot and challenging issue due to the ubiquitous emerging of mobile devices and free Wi-Fi services in shopping centers in recent years. Aimed at solving the indoor trajectory prediction problem, in this paper, a hybrid method based on Hidden Markov approach is proposed. The proposed approach clusters Wi-Fi access points according to their similarities first; then, a frequent subtrajectory based HMM which captures the moving patterns of users has been investigated. In addition, we assume that a customer’s visiting history has certain patterns; thus, we integrate trajectory prediction with shop category prediction into a unified framework which further improves the predicting ability. Comprehensive performance evaluation using a large-scale real dataset collected between September 2012 and October 2013 from over 120,000 anonymized, opt-in consumers in a large shopping center in Sydney was conducted; the experimental results show that the proposed method outperforms the traditional HMM and perform well enough to be usable in practice.

While a large number of works concentrated on forecasting trajectories in the outdoor environment, predicting the movement of users in indoor settings has attracted much more attention recently because of the development of smartphones and maturity of Wi-Fi services, e.g., in office buildings. Predicting a user’s movement in indoor spaces can not only help better understand his/her intentions but also improve his/her living experience. While most of the prediction approaches to date tackle the problem by constructing the mathematical models to learn the mobility of objects, they cannot efficiently model the movement of indoor users in the constraint but filled with spatial-temporal-semantic info settings. In order to solve this issue, we propose a frequent subtrajectory-based Markov model that incorporates the spatial location, the temporal aspect, and the shop category context into a unified framework. We first present the frequent subtrajectory algorithm to model and predict adjacent moving points from physical movement perspective, and then, by taking the duration of stay at a specific location into account, we further improve the prediction precision. Finally, by taking location context in the indoor environment (e.g., shop categories) into consideration, we successfully model and predict the user’s future visiting points from the semantic perspective. To validate the effectiveness of our model, we conduct a complete evaluation on a large-scale real-world dataset with more than 261,269 trajectories collected from over 120,000 customers in a shopping mall. The experiment results demonstrate that our method performs significantly superior prediction performance comparing the state-of-the-art models.

SummaryWith the continuous increase of the network scale, the structure of the network has also become complicated. The original community discovery algorithm based on small‐scale static networks has been unable to meet our needs. In order to improve the quality of community division, community discovery algorithms based on multiple optimization functions have been proposed. These multiobjective algorithms have continued to increase in time complexity as the optimization functions increase. The time complexity of the multiobjective community discovery algorithm is reduced, and the particle swarm algorithm has higher efficiency and accuracy in solving multiobjective optimization (MOO) problems. Based on the above background, the purpose of this article is to study a multiobjective particle swarm community discovery algorithm based on representation learning. This article uses the network representation learning method for static network community discovery, and designs an improved multiobjective particle swarm‐based community discovery algorithm (MOPSO‐CD). This randomness effectively prevents the algorithm from falling into a local optimum. At the same time, combined with the MOO algorithm, all the Pareto optimal solution sets are retained to adjust the population to correct the lack of accuracy caused by the randomness of the algorithm. In addition, in order to improve the efficiency of the algorithm, this article introduces an efficient Pareto optimal solution set method. Compared with the traditional MOO strategy, the time complexity of the MOO process is O(n2) Reduced to O(nlogn). Through experimental analysis, MOPSO‐CD has higher efficiency and community discovery quality.

In recent years, local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks. However, there are still some issues that need to be further studied. First, there is no local community detection algorithm dedicated to detecting a seed-oriented local community, that is, the local community with the seed as the core. The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters, respectively. To solve the existing problems, we propose a seed-oriented local community detection algorithm, named SOLCD, that is based on influence spreading. First, we propose a novel measure of node influence named k-core centrality that is based on the k-core value of adjacent nodes. Second, we obtain the seed-oriented local community, which is composed of the may-members and the must-member chain of the seed, by detecting the influence scope of the seed. The may-members and the must-members of the seed are determined by judging the influence relationship between the node and the seed. Five state-of-art algorithms are compared to SOLCD on six real-world networks and three groups of artificial networks. The experimental results show that SOLCD can achieve a high-quality seed-oriented local community for various real-world networks and artificial networks with different parameters. In addition, when taking nodes with different influence as seeds, SOLCD can stably obtain high-quality seed-oriented local communities.

Support vector machine (SVM) is a robust machine learning method and is widely used in classification. However, the traditional SVM training methods may reveal personal privacy when the training data contains sensitive information. In the training process of SVMs, working set selection is a vital step for the sequential minimal optimization-type decomposition methods. To avoid complex sensitivity analysis and the influence of high-dimensional data on the noise of the existing SVM classifiers with privacy protection, we propose a new differentially private working set selection algorithm (DPWSS) in this paper, which utilizes the exponential mechanism to privately select working sets. We theoretically prove that the proposed algorithm satisfies differential privacy. The extended experiments show that the DPWSS algorithm achieves classification capability almost the same as the original non-privacy SVM under different parameters. The errors of optimized objective value between the two algorithms are nearly less than two, meanwhile, the DPWSS algorithm has a higher execution efficiency than the original non-privacy SVM by comparing iterations on different datasets. To the best of our knowledge, DPWSS is the first private working set selection algorithm based on differential privacy.

The measurement of node importance in complex networks and the influence maximization problem are hot issues in the field of network science in recent years. At present, node importance can usually be measured by degree index, betweenness index, tightness index, etc. In addition, K-shell, PageRank, eigenvector centrality and other indicators can also be used to evaluate node importance. However, these evaluation indicators have their own limitations. Based on K-shell, this paper proposes a heuristic algorithm -- KDP algorithm, which not only combines the diversity of the distribution of adjacent nodes, but also takes into account the importance of its neighbor nodes. In this paper, the independent cascade model is used to carry out the simulation experiment of node influence on multiple real complex networks. The experiment proves that KDP index can measure the propagation ability of nodes more accurately and has good time efficiency compared with other node importance evaluation indexes.

Support vector machines (SVMs) are among the most robust and accurate methods in all well-known machine learning algorithms, especially for classification. The SVMs train a classification model by solving an optimization problem to decide which instances in the training datasets are the support vectors (SVs). However, SVs are intact instances taken from the training datasets and directly releasing the classification model of the SVMs will carry significant risk to the privacy of individuals, when the training datasets contain sensitive information. In this paper, we study the problem of how to release the classification model of kernel SVMs while preventing privacy leakage of the SVs and satisfying the requirement of privacy protection. We propose a new differentially private algorithm for the kernel SVMs based on the exponential and Laplace hybrid mechanism named DPKSVMEL. The DPKSVMEL algorithm has two major advantages compared with existing private SVM algorithms. One is that it protects the privacy of the SVs by postprocessing and the training process of the non-private kernel SVMs does not change. Another is that the scoring function values are directly derived from the symmetric kernel matrix generated during the training process and does not require additional storage space and complex sensitivity analysis. In the DPKSVMEL algorithm, we define a similarity parameter to denote the correlation or distance between the non-SVs and every SV. And then, every non-SV is divided into a group with one of the SVs according to the maximal value of the similarity. Under some certain similarity parameter value, we replace every SV with a mean value of the top-k randomly selected most similar non-SVs within the group by the exponential mechanism if the number of non-SVs is greater than k. Otherwise, we add random noise to the SVs by the Laplace mechanism. We theoretically prove that the DPKSVMEL algorithm satisfies differential privacy. The extensive experiments show the effectiveness of the DPKSVMEL algorithm for kernel SVMs on real datasets; meanwhile, it achieves higher classification accuracy than existing private SVM algorithms.

Recommendation algorithm is not only widely used in entertainment media, but also plays an important role in national strategy, such as the recommendation algorithm of byte beating company. This paper studies the personalized recommendation algorithm based on representation learning. The data in social network is complex, and the data mainly exists in various platforms. This paper introduces AI (Artificial Intelligence) algorithm to guide the algorithm of representation learning, and integrates the algorithm steps of representation learning, to realize the implementation of personalized recommendation algorithm in social network, and compares the representation learning algorithm. Finally, this paper designs a method based on heat conduction and text mining to provide users with webpage recommendations and help users better mine interesting popular webpages. Research shows that the performance of IMF is better than that of PMF because it overcomes the sparsity of data by pre-filling. The accuracy of IMF is 3.69% higher than that of PMF on the epinions data set, and 6.24% higher than that of PMF on the double data set. Rtcf, socialmf, tcars, CSIT, isrec, and hesmf have better performance than PMF and IMF. Among them, rtcf, socialmf, tcars, CSIT, isrec, and hesmf improve the MAE performance of PMF by 7.6%, 6.3%, 8.8%, 7.9%, 9.5% and 14.2%, respectively.

Predicting the further whereabouts of a large scale of users in indoor spaces has just been a hot and challenging issue in recent years due to the ubiquitous emerging of mobile devices and free Wi-Fi services, e.g. in shopping centers. While the existing prediction algorithms focusing on modeling the movement of users via mathematical models are useful in the outdoor environment, they fall short in predicting the position of indoor moving objects in a constraint but full of spatial-semantic information environment. To tackle this problem, we present a similarity based model by incorporating the spatial and the location contexts into a unified framework. We first present a novel trajectory similarity method which considers the spatial and contextual information in the indoor settings, then based on the similarities we present a clustering algorithm to group the trajectories, finally the most similar trajectory is returned for the prediction. In order to evaluate the precision of our proposed method, we designed a comprehensive performance evaluation using a large-scale dataset collected between September 2012 and October 2013 from over 120,000 anonymized, opt-in consumers in a large supermarket. Results show that our approach achieves a much better trajectory prediction performance against the baseline methods.

Weighted network contains a lot of sensitive information and may seriously jeopardize individual privacy. In this paper, we study the problem of differential privacy for weighted network. We found most existing methods add noise to edge weights directly and neglect the structural role of node. These methods perform with low accuracy. To address the above issue, we propose two approaches. One approach describes a differential privacy method for Stochastic Block Model. This private SBM reveals and the structural role of node and respects the privacy of it. Another approach develops a differential privacy method for weighted network through structuring a private probability model. We use Variational Bayes to learn the private model parameters. It adds noise to the parameters of the probability model instead of edge weights, and achieve high data utility. Experiments on real datasets illustrate that our algorithm privately releases weighted network and achieves high accuracy.