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The TEST pervasive use of social media has highlighted the importance of developing sophisticated models for early information warning systems within online communities. Despite the advancements that have been made, existing models often fail to adequately consider the pivotal role of network topology and temporal dynamics in information dissemination. This results in suboptimal predictions of content propagation patterns. This study introduces the User Propagation Influence-based Linear Threshold (UPI-LT) model, which represents a novel approach to the simulation of information spread. The UPI-LT model introduces an innovative approach to consider the number of active neighboring nodes, incorporating a time decay factor to account for the evolving influence of information over time. The model's technical innovations include the incorporation of a homophily ratio, which assesses the similarity between users, and a dynamic adjustment of activation thresholds, which reflect a deeper understanding of social influence mechanisms. Empirical results on real-world datasets validate the UPI-LT model's enhanced predictive capabilities for information spread. [ABSTRACT FROM AUTHOR]

Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of "Temporal Information Entropy (TIE)", integrating it into the Node2Vec algorithm's random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value. [ABSTRACT FROM AUTHOR]

The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and challenging. In contrast to the link prediction in cross-sectional networks, dynamic link prediction methods need to cater to the actor-level temporal changes and associated evolutionary information regarding their micro- (i.e., link formation/deletion) and mesoscale (i.e., community formation) network structure. With the advent of abundant community detection algorithms, the research community has examined community-aware link prediction strategies in static networks. However, the same task in dynamic networks where, apart from the actors and links among them, their community pattern is also dynamic, is yet to be explored. Evolutionary community-aware information, including the associated link structure and temporal neighborhood changes, can effectively be mined to build dynamic similarity metrics for dynamic link prediction. This study aims to develop and integrate such dynamic features with machine learning algorithms for link prediction tasks in dynamic social networks. It also compares the performances of these features against well-known similarity metrics (i.e., ResourceAllocation) for static networks and a time series-based link prediction strategy in dynamic networks. These proposed features achieved high-performance scores, representing them as prospective candidates for both dynamic link prediction tasks and modeling the network growth. [ABSTRACT FROM AUTHOR]

Age is an essential piece of demographic information for social profiling, as different social and behavioral characteristics are age-related. To acquire age information, most of the previously conducted social profiling studies have predicted age information. However, age predictions in social profiling have been very limited, because it is difficult or impossible to obtain age information from social media. Moreover, age-prediction results have rarely been used to study human dynamics. In these circumstances, this study focused on naver.com, a nationwide social media website in Korea. Although the social profiles of news commenters on naver.com can be analyzed and used, the age information is incomplete (i.e., partially open to the public) owing to anonymity and privacy protection policies. Therefore, no prior research has used naver.com for age predictions or subsequent analyses based on the predicted age information. To address this research gap, this study proposes a method that uses a machine learning approach to predict the age information of anonymous commenters on unlabeled (i.e., with age information hidden) news articles on naver.com. Furthermore, the predicted age information was fused with the section information of the collected news articles, and fuzzy differences between age groups were analyzed for topics of interest, using the proposed correlation–similarity matrix and fuzzy sets of age differences. Thus, differentiated from the previous social profiling studies, this study expands the literature on social profiling and human dynamics studies. Consequently, it revealed differences between age groups from anonymous and incomplete Korean social media that can help in understanding age differences and ease related intergenerational conflicts to help reach a sustainable South Korea. [ABSTRACT FROM AUTHOR]

Social cognition is fundamental in everyday life to understand "others' behavior", which is a key feature of social abilities. Previous studies demonstrated the efficacy of a rehabilitative intervention in semi-immersive virtual reality (VR) controlled by whole-body motion to improve the ability of patients with cerebellar disorders to predict others' intentions (VR-SPIRIT). Patients with severe ataxia that have difficulties at multiple levels of social processing could benefit from this intervention in terms of improving their social prediction skills, but they may have difficulties in controlling VR with whole-body movements. Therefore, we implemented VR-SPIRIT on a wearable, affordable, and easy-to-use technology, such as the Oculus Quest, a head-mounted display. The aim of this work was to evaluate the usability and tolerability of this VR application. We recruited 10 patients (37.7 ± 14.8 years old, seven males) with different types of hereditary ataxia who performed a single VR-SPIRIT session using the Oculus Quest viewer. After the session, patients answered a series of questionnaires to investigate the overall usability of the system and its potential effects in terms of cyber sickness. The preliminary results demonstrated system usability and tolerability. Indeed, only three patients did not complete the session due to different problems (dizziness, nausea, and boredom). In future studies, more patients will be enrolled to assess the effectiveness of the application, paving the way for the implementation of social training that can also be delivered at home. [ABSTRACT FROM AUTHOR]

Due to the great advances in information technology, an increasing number of social platforms have appeared. Friend recommendation is an important task in social media, but newly built social platforms have insufficient information to predict entity relationships. In this case, platforms with sufficient information can help newly built platforms. To address this challenge, a model of link prediction in social multiplex networks (LPSMN) is proposed in this work. Specifically, we first extract graph structure features, latent features and explicit features and then concatenate these features as link representations. Then, with the assistance of external information from a mature platform, an attention mechanism is employed to construct a multiplex and enhanced forecasting model. Additionally, we consider the problem of link prediction to be a binary classification problem. This method utilises three different kinds of features to improve link prediction performance. Finally, we use five synthetic networks with various degree distributions and two real-world social multiplex networks (Weibo–Douban and Facebook–Twitter) to build an experimental scenario for further assessment. The numerical results indicate that the proposed LPSMN model improves the prediction accuracy compared with several baseline methods. We also find that with the decline in network heterogeneity, the performance of LPSMN increases. [ABSTRACT FROM AUTHOR]

One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the underlying growth mechanisms of social networks. Mining the temporal patterns of dynamic networks has led researchers to utilise dynamic information for dynamic link prediction. Despite several methodological improvements in dynamic link prediction, temporal variations of actor-level network structure and neighbourhood information have drawn little attention from the network science community. Evolutionary aspects of network positional changes and associated neighbourhoods, attributed to non-connected actor pairs, may suitably be used for predicting the possibility of their future associations. In this study, we attempted to build dynamic similarity metrics by considering temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics then worked as dynamic features in the supervised link prediction model, and performances were compared against static similarity metrics (e.g., AdamicAdar). Improved performance is achieved by the metrics considered in this study, representing them as prospective candidates for dynamic link prediction tasks and to help understand the underlying evolutionary mechanism. [ABSTRACT FROM AUTHOR]

Twitter, Instagram and Facebook are expanding rapidly, reporting on daily news, social activities and regional or international actual occurrences. Twitter and other platforms have gained popularity because they allow users to submit information, links, photos and videos with few restrictions on content. As a result of technology advances ("big" data) and an increasing trend toward institutionalizing ethics regulation, social network analysis (SNA) research is currently confronted with serious ethical challenges. A significant percentage of human interactions occur on social networks online. In this instance, content freshness is essential, as content popularity declines with time. Therefore, we investigate how influencer content (i.e., posts) generates interactions, as measured by the number of likes and reactions. The Gradient Boosted Decision Tree (GBDT) and the Chaotic Gradient-Based Optimizer are required for estimation (CGBO). Using earlier group interactions, we develop the Influencers Prediction issue in this study's setting of SN-created groups. We also provide a GBDT-CGBO framework and an efficient method for identifying users with the ability to influence the future behaviour of others. Our contribution is based on logic, experimentation and analytic techniques. The goal of this paper is to find domain-based social influencers using a framework that uses semantic analysis and machine learning modules to measure and predict users' credibility in different domains and at different times. To solve these problems, future research will have to focus on co-authorship networks and economic networks instead of online social networks. The results show that our GBDT-CGBO method is both useful and effective. Based on the test results, the GBDT-CGBO model can correctly classify unclear data, which speeds up processing and makes it more efficient. [ABSTRACT FROM AUTHOR]

Social prediction is a key feature of social cognition (SC), a function in which the modulating role of the cerebellum is recognized. Accordingly, cerebellar alterations are reported in cerebellar pathologies, neurodevelopmental disorders, and psychiatric conditions that show SC deficits. Nevertheless, to date, no study has directly compared populations representative of these three conditions with respect to SC and cerebellar alterations. Therefore, the present exploratory study aimed to compare the SC profiles of individuals with cerebellar neurodegenerative disorders (CB), autism (ASD), bipolar disorder type 2 (BD2), or healthy subjects (HS) using a battery of social tests requiring different degrees of prediction processing. The patterns of cerebellar gray matter (GM) alterations were compared among the groups using voxel-based morphometry. Compared to HS, the clinical groups showed common SC deficits in tasks involving a moderate to high level of prediction. The behavioral results of the clinical groups are consistent with the presence of overlapping GM reduction in cerebellar right Crus II, an area notably involved in complex social processing and prediction. Although exploratory and preliminary, these results deepen the cerebellar role in social prediction and highlight the transdiagnostic value of the cerebellum in social functioning and prediction in pathologies of different aetiologies, forecasting novel possibilities for shared interventions. [ABSTRACT FROM AUTHOR]

Following the large-scale 2015–2016 migration crisis that shook Europe, deploying big data and social media harvesting methods became gradually popular in mass forced migration monitoring. These methods have focused on producing 'real-time' inferences and predictions on individual and social behavioral, preferential, and cognitive patterns of human mobility. Although the volume of such data has improved rapidly due to social media and remote sensing technologies, they have also produced biased, flawed, or otherwise invasive results that made migrants' lives more difficult in transit. This review article explores the recent debate on the use of social media data to train machine learning classifiers and modify thresholds to help algorithmic systems monitor and predict violence and forced migration. Ultimately, it identifies and dissects five prevalent explanations in the literature on limitations for the use of such data for A.I. forecasting, namely 'policy-engineering mismatch', 'accessibility/comprehensibility', 'legal/legislative legitimacy', 'poor data cleaning', and 'difficulty of troubleshooting'. From this review, the article suggests anonymization, distributed responsibility, and 'right to reasonable inferences' debates as potential solutions and next research steps to remedy these problems. [ABSTRACT FROM AUTHOR]

The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the government and enterprises' decision makers to develop an optimal strategy for electric energy planning work in advance. According to the data of the whole social electricity consumption in Jiangsu Province of China from 2015 to 2019, this paper uses the improved particle swarm optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and establishes a metabolic FGM (1, 1) model to predict the whole social electricity consumption in Jiangsu Province of China from 2020 to 2023. The results show that in the next few years the whole social electricity consumption in Jiangsu Province will show a growth trend, but the growth rate will slow down generally. It can be seen that the prediction accuracy of the metabolic FGM (1, 1) model is higher than that of the GM (1, 1) and FGM (1, 1) models. In addition, the paper analyzes the reasons for the changes in the whole society electricity consumption in Jiangsu Province of China and provides support for government decision making. [ABSTRACT FROM AUTHOR]

Previous research suggests that predictive mechanisms are essential in perceiving social interactions. However, these studies did not isolate action prediction (a priori expectations about how partners in an interaction react to one another) from action integration (a posteriori processing of both partner's actions). This study investigated action prediction during social interactions while controlling for integration confounds. Twenty participants viewed 3D animations depicting an action–reaction interaction between two actors. At the start of each action–reaction interaction, one actor performs a social action. Immediately after, instead of presenting the other actor's reaction, a black screen covers the animation for a short time (occlusion duration) until a still frame depicting a precise moment of the reaction is shown (reaction frame). The moment shown in the reaction frame is either temporally aligned with the occlusion duration or deviates by 150 ms or 300 ms. Fifty percent of the action–reaction trials were semantically congruent, and the remaining were incongruent, e.g., one actor offers to shake hands, and the other reciprocally shakes their hand (congruent action–reaction) versus one actor offers to shake hands, and the other leans down (incongruent action–reaction). Participants made fast congruency judgments. We hypothesized that judging the congruency of action–reaction sequences is aided by temporal predictions. The findings supported this hypothesis; linear speed-accuracy scores showed that congruency judgments were facilitated by a temporally aligned occlusion duration, and reaction frames compared to 300 ms deviations, thus suggesting that observers internally simulate the temporal unfolding of an observed social interction. Furthermore, we explored the link between participants with higher autistic traits and their sensitivity to temporal deviations. Overall, the study offers new evidence of prediction mechanisms underpinning the perception of social interactions in isolation from action integration confounds. [ABSTRACT FROM AUTHOR]

This paper presents the results of a systematic literature review on power distribution and power dynamics in multiparty systems. Multiparty systems are underorganized social structures in which power dynamics unfold and impact collaboration effectiveness. We use a theory-driven approach to integrate the empirical literature that explored power differences and dynamics in multiparty systems and we have a two-fold contribution to literature. First, we explore the way power is conceptualized in multiparty systems. Second, we investigate which predictions and propositions of the Social Distance Theory of Power and the Approach Inhibition Model of Power can be used to integrate research on power distribution and dynamics in multiparty systems. We extend the predominantly experimental empirical support of these two theories with insights from the multiparty systems literature. With respect to the way in which power is conceptualized in the multiparty systems literature, our study shows a shift from a possession over resources to a relational perspective on power in the last decades. Moreover, based on the insights of the two psychological theories of power, the study reflects upon the benefits and drawbacks of high versus low power for collaboration effectiveness among stakeholders, pointing towards ways in which facilitators can work with power differences in multiparty systems. Finally, the study points toward directions for future research concerning power dynamics in multiparty systems. [ABSTRACT FROM AUTHOR]

This study explored how the success of project crowdfunding can be predicted based on the texts of Internet social welfare crowdfunding projects. Through a calculation of the quantity of information and a mining of the sentimental value of the text, how the text information of the interconnected social welfare crowdfunding project affects the success of the project was studied. To this aim, a sentimental dictionary of Chinese Internet social welfare crowdfunding texts was constructed, and information entropy was used to calculate the quantity of information in the text. It was found that, compared with the information presented in the text, the fundraiser's social network factors are key in improving the success of fundraising. The sentimental value of the text positively affects the success of fundraising, while the influence of the quantity of information is represented as an inverted, U-shaped relationship. The non-ideal R-squared indices reflected that the multiple linear regression models do not perform well regarding this prediction. Furthermore, this paper validated and analyzed the prediction efficiency of four machine-learning models, including a multiple regression model, a decision tree regression model, a random forest regression model, and an AdaBoost regression model, and the AdaBoost regressor showed the best efficiency, with an accuracy R2 of up to 97.7%. This study provides methods for the quantified processing of information contained in social welfare crowdfunding texts, as well as effective prediction on social welfare crowdfunding, and also seeks to raise the success rate of crowdfunding and thus features commercial and social value. [ABSTRACT FROM AUTHOR]

Rural community population forecasting has important guiding significance to rural construction and development. In this study, a novel grey Bernoulli model combined with an improved Aquila Optimizer (IAO) was used to forecast rural community population in China. Firstly, this study improved the Aquila Optimizer by combining quasi-opposition learning strategy and wavelet mutation strategy, and proposed the new IAO algorithm. By comparing with other algorithms on CEC2017 test functions, the proposed IAO algorithm has the advantages of faster convergence speed and higher convergence accuracy. Secondly, based on the data of China's rural community population from 1990 to 2019, a consistent fractional accumulation nonhomogeneous grey Bernoulli model called CFANGBM(1, 1, b, c) was established for rural population forecasting. The proposed IAO algorithm was used to optimize the parameters of the model, and then the rural population of China was predicted. Four error measures were used to evaluate the model, and by comparing with other forecasting models, the experimental results show that the proposed model had the smallest error between the forecasted value and the real value, which illustrates the effectiveness of using the IAO algorithm to solve CFANGBM(1, 1, b, c). At the end of this paper, the forecast data of China's rural population from 2020 to 2024 are given for reference. [ABSTRACT FROM AUTHOR]

Predicting the popularity of online content is an important task for content recommendation, social influence prediction and so on. Recent deep learning models generally utilize graph neural networks to model the complex relationship between information cascade graph and future popularity, and have shown better prediction results compared with traditional methods. However, existing models adopt simple graph pooling strategies, e.g., summation or average, which prone to generate inefficient cascade graph representation and lead to unsatisfactory prediction results. Meanwhile, they often overlook the temporal information in the diffusion process which has been proved to be a salient predictor for popularity prediction. To focus attention on the important users and exclude noises caused by other less relevant users when generating cascade graph representation, we learn the importance coefficient of users and adopt sample mechanism in graph pooling process. In order to capture the temporal features in the diffusion process, we incorporate the inter-infection duration time information into our model by using LSTM neural network. The results show that temporal information rather than cascade graph information is a better predictor for popularity. The experimental results on real datasets show that our model significantly improves the prediction accuracy compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source. [ABSTRACT FROM AUTHOR]

Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals' uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks. [ABSTRACT FROM AUTHOR]

With the popularization of 5G communications, the scale of social networks has grown rapidly, and the types of messages have become increasingly complex. The rapid increases in the number of access nodes and the amount of data have put a greater burden on the transmission of information in the networks. However, when transferring data from a large number of users, the performance of traditional opportunistic network routing algorithms is insufficient, which often leads to problems such as high energy consumption, network congestion, and data packet loss. The way in which to improve this transmission environment has become a difficult task. Therefore, in order to ensure the effective transmission of data and reduce network congestion, this paper proposed a link prediction model based on triangular relationships in opportunistic social networks (LPMBT). In the topological structure of the social network, the algorithm scores links based on the frequency of use and selects the optimal relay node based on the score. It can also efficiently track the target node and reconstruct the sub-community. The simulation experimental results showed that the algorithm had excellent performance, effectively reduced overhead, reduced the end-to-end delay, and greatly improved the data transfer rate in the opportunistic network. [ABSTRACT FROM AUTHOR]

Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model's performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction. [ABSTRACT FROM AUTHOR]