The formation of group consensus plays a pivotal role in various fields such as social decision-making and political elections, exerting a profound impact on societal stability and development. To investigate the mechanisms and influencing factors behind the achievement of group consensus, in this paper we propose a novel opinion evolution model based on the sleeper effect, namely SEOE. The impact of factors such as agents’ confidence bounds, the number of opinion leaders, and the opinion values of opinion leaders on opinion evolution are explored by extensive simulation experiments on both artificially generated networks and real social networks. The results of the paper can provide guidance for social management and decision-making.

Inspired by the Transformer's adaptive capability in natural language processing and computer vision domains, and supported by CLIP's strong semantic prior knowledge, we propose a novel generative model named STMotionFormer based on improved Transformer block and CLIP for language-guided 3D skeleton-based human motion synthesis. The skeleton sequence, as a kind of spatial temporal dynamic data, not only contains temporal information but also consists of a natural graph structure by different body parts. We focus on this, propose Temporal Transformer block (T-Former) applies attention mechanism along the temporal dimension to capture the long-term relationship, and propose spatial Transformer block (S-Former) applies attention mechanism in the spatial dimension to aggregate and update joints' spatial features. Therefore, our approach can automatically explore both spatial and temporal information, which can generate skeleton sequence with reasonable graph structure as well as global time relationship. Moreover, powerful semantic prior knowledge of CLIP are injected into our motion-language joint manifold. We evaluate our method on the public KIT motion language dataset that contains manually annotated 3D pose sequences. Experimental results show that our model outperforms the state-of-the-art in terms of APE and AVE respectively. Qualitative visualization results indicate that our model can generate human motions that are more compatible with semantic information even than the GroundTruth.

Opinion dynamics, which concerns how opinions evolve and spread in social networks has been widely studied during past years, and a lot of classical models have been proposed to describe the opinion diffusion process. However, most existing leader-follower-relationship based models ignore the influence of normal individuals and do not consider the feedback effect of opinion difference on individuals, which are important for opinion spread. In this paper, inspired by two well-known social theories: Emotional Mobilization and Social Judgement Theory, we first propose a method to identify individual influence factor based on both network topology and personal behavior attributes. Then, we propose a novel opinion evolution model named Dynamic Adaptive Individual Weighting model which focuses on individual heterogeneity and considers the opinion difference assimilation effect. In this model, the influence weight of an agent's neighbour on the agent can be dynamically affected by their opinion difference and adaptively adjusted based on the neighbour's relative influence factor. Moreover, environmental noise is also introduced to assimilate realistic situations' uncertainty. Experimental results on 12 real and 2 generated network datasets show that our proposed model can precisely reflect the evolution process and trend of opinions over different social networks. The study can enable decision-makers better understand the fundamental processes of opinion diffusion and design more efficient strategies for political or business activities.

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type independently. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that there may exist some latent relevance among relations in KG. It may not necessary nor effective to consider all relation types for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which clusters relations with latent relevance to generates virtual relations. Specifically, we first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for node encoding on VRKGs, which iteratively updates a node embedding only depending on the node itself and its neighbors, but involve no additional training parameters. LWS mechanism is also employed on a user-item bipartite graph for user representation learning, which utilizes item encodings with virtual relational knowledge to help train user representations. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

Persuasion, which is the act of convincing someone to change their idea, attitude, or opinion, happens anywhere and anytime in social life. In this paper, based on the social judgment theory, we propose a novel asynchronous evolution Deffuant-Weisbuch opinion dynamics model by introducing the latitude of non-commitment. In our model, when agents communicate with each other, the evolution of their opinions depend not only on the opinion of themselves and their neighbors but also on the latitude where they locate, thus an opinion evolution rule that close to reality is designed. The model can well explain the persuasion process and captures the assimilation phenomenon in social networks. Simulation results show that the model can finally reach a stable state. Moreover, network topology, initial distribution of agents’ opinions and the range of latitudes of non-commitment all have an impact on the convergence time of opinions.

The classic Hegselmann–Krause opinion dynamics model plays an important role in analyzing opinion evolution among people. However, it is not in line with the reality of interpersonal communication during the modern online social times, in which people are easy to exchange opinions with another one who agrees or disagrees with themselves on certain issues by social networks. In this paper, considering the effects of the one-sided versus two-sided presentation of a controversial issue, we improve the classic HK model and propose a novel information-type education-based Hegselmann–Krause model. In our work, we formulate rules of how agents’ opinions change and carry out simulation experiments on networks with different proportions of high-educated agents. Extensive experiments on artificially generated networks show that it is more difficult for someone to reach a consensus with another one who is within a group with a higher proportion of low-educated people than with a higher proportion of high-educated people, which verify the effectiveness of the proposed model.

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and lead to a tsunami of online social media. Individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. Many researches try to analyze the sentiment of the COVID-19-related content from these social networks. However, they have rarely focused on the vaccine. In this paper, we study the COVID-19 vaccine topic from Twitter. Specifically, all the tweets related to COVID-19 vaccine from December 15th, 2020 to February 10th, 2021 are collected by using the Twitter API, then the unsupervised learning VADER model is used to judge the emotion categories (positive, neutral, negative) and calculate the sentiment value of the dataset. Based on the interaction between users, a communication topological network is constructed and the emotional direction is explored. We find that people had different sentiments between Chinese vaccine and those in other countries. The sentiment value might be affected by the number of daily news cases and deaths, the nature of key issues in the communication network. And revealing that the key nodes in the social network can produce emotional contagion to other nodes.

The existing opinion dynamics models based on the impact of opinion leaders tend to only consider the impact of opinion leaders on normal individuals, but ignore the impact of normal individuals on opinion leaders. Normal individuals in social networks can also change the opinions of opinion leaders by initiative affection mobilization. In this paper, an affection mobilization leadership index (AMLI) is used to identify opinion leaders and influential agents who can mobilize affection initiatively. A novel opinion dynamics affection mobilization-based Hegselmann-Krause model (AMHK) is then proposed. Extensive experiments on both artificially generated and real-trace network datasets verify the effectiveness and efficiency of the proposed model. Appropriate proportion of influential agents can promote the reach of consensus, while excess influential agents could lead a consensus with fragmented opinions, which provides a significant train of thought in guiding public opinion.

Hegselmann-Krause model plays an important role in opinion dynamics. Many researchers try to improve the classic Hegselmann-Krause model from different aspects. However, the influence of agents' media literacy on the opinion evolution always have not been taken into consideration. Due to the differences in accessing, analyzing, and producing information between agents, the media literacy gap will evidently affect their communication in real life. In this paper, media literacy is introduced to improve the traditional HK model, and a novel opinion dynamics Hegselmann-Krause model with Media Literacy (HKML) is proposed. In our work we not only consider the confidence bound, but also consider the media literacy of agents. Agents under the HKML model can select and communicate with influential neighbors through a more accuracy criterion. Numerical simulation results demonstrate that the HKML model breaks through the limit of the confidence bound, which makes more communication with less convergence time. Moreover, the HKML model shows strong robustness with the environmental noise.

As people spend considerable time on digital media, online platforms have functioned as major channels for public expression. Understanding the formation and diffusion of sentiment or emotion is benefit to create a healthy environment for public discussion. In this paper, based on emotion contagion and opinion dynamics, we established a specially designed model for dynamic emotion diffusion. The method of computational modelling and simulation is applied to simulate the real-world social interactions. Factors including the distribution of initial emotion, credibility threshold, self-assertiveness and emotion decay function that might influence sentiment diffusion are analyzed, a novel concept of emotion fluctuation is introduced to predict agents' behavior. Simulation results demonstrate that all these factors mentioned above can influence the evolution of emotion, leading to its fragmentation, polarization or consensus.

The optimal node placement for a very large plane without boundary effect has been proven to be the regular triangular-lattice pattern in 1939. However, the regular triangular-lattice placement may not be optimal in a long belt with an upper and lower boundary. This paper proposes an optimal node deployment pattern to minimize the number of nodes for completely covering a long belt. The optimal pattern uses shifted node strips for belt coverage, and we compute the best node distance, strip offset, and strip distance for different belt heights. Mathematical analysis are provided to prove its optimality in terms of the minimum node density for belt coverage. Numerical computations are used to show its superiority, compared with other well-known placement patterns and our previously proposed equipartition placement.

Subarea localization has been recently proposed to locate a mobile device within a certain subarea delimitated by the overlapping ranges of monitoring nodes. In our previous work, we have proposed a divide-and-cover node placement for complete coverage of a long belt, but the subarea localization has not been considered in the design of such a placement. This paper studies the subarea localization performance for the divide-and-cover deployment. We first obtain a formula to compute the mean subarea localization error of the whole belt and then analyze the optimal placement parameter to minimize the subarea localization error. Theoretical analysis show that compared with other node placement including the well-known regular triangular-lattice placement, the proposed scheme can achieve a lower mean localization error. Field experiments validate the effectiveness of the proposed scheme.

Barrier coverage is an important topic in wireless sensor networks. When sensors are randomly deployed, barrier gaps may occur if the number of deployed sensors is not large enough or some sensors start malfunctioning or run out of energy. How to efficiently mend these barrier gaps is an important research issue. In this paper, we study the gap mending problem in a hybrid sensor network which consists of both stationary and mobile sensors with adjustable sensing ranges. We propose two gap mending schemes: the min-max scheme and the max-lifetime scheme. The first is to minimize the maximal energy consumption to move sensors, and the second is to maximize the lifetime of barrier coverage after mending all gaps. Simulation results show that the min-max scheme can achieve a lower maximal moving distance and the max-lifetime scheme can efficiently extend the barrier lifetime.

Coverage is an important issue in many wireless networks. In this paper, we address the problem of node placement for ensuring complete coverage in a long belt scenario and propose a novel placement approach to minimize the number of nodes needed. In our work, each node is assumed to be able to cover a disk area centered at itself with a fixed radius, then a divide-and-cover node placement method is proposed. In the proposed method, a long belt is divided into some sub-belts (if necessary), and then a string of nodes are placed parallel to the long side of each sub-belt to completely cover the sub-belt. We then determine the optimal distance between two adjacent nodes in a string and the number of such strings to minimize the number of nodes for complete belt coverage. Theoretical proofs and analysis show that compared with other node placement including the well-known regular triangular-lattice placement, the proposed method can achieve lower node density in some cases when the belt height is not very large. A combination of the proposed method and the triangular-lattice placement is then proposed, and the optimal ranges of the belt height for their respective applications to achieve the lowest node density are computed.

Energy-efficient barrier coverage is an important issue in wireless sensor networks. In this paper we study the problem of how to maximize the lifetime of a barrier, where sensors have adjustable sensing ranges. In our approach, each node is divided into some virtual sub-nodes according to the available sensing ranges. For small-scale sensor networks, we construct a barrier coverage graph where a link exists between two sub-nodes in two different nodes, if their sensing ranges overlap. We propose to use a linear programming optimization method based on the exhaustive search of all possible barriers in the constructed graph to find the optimal barriers and their respective operation times. For large-scale sensor networks, we propose two distributed heuristics: one is to randomly select, from its neighboring sub-nodes, a next sub-node to construct barriers; another is to greedily select a next sub- node to best match the lifetime of the barrier constructed before choosing this sub-node. Simulation results show that compared with the randomized one, the greedy scheme can achieve longer lifetime and lower message overhead.