The era of digital intelligence offers new opportunities for the development of social computing and social intelligence.Cross-disciplinary fusion shall be a critical approach for its deep development.This paper elaborates the connotation and denotation of social computing, discusses the paradigm shift of social computing research, and the general development of social computing and social intelligence.Next, it looks forward to the social computing and social intelligence in the era of digital intelligence, and proposes three pillars for constructinga social intelligence system based on the new infrastructure.The construction of such an intelligent system is mainly composed by three components, including the construction of large-scale high-velocity data intelligence, the integration of multi-scale flexible spatial intelligence, and the formation of complex adaptive social intelligence.There is a level progression from data intelligence to social intelligence, in which data, computing and society are entangled.As such, computing science, data science, spatial science, complex science and social science are requested to interact from both theoretical and methodological perspectives.With the rapid update of digital-intelligent technologies and their penetration into the whole social-economic system, social computing and social intelligence is bound to seek breakthrough and deep development in the interdisciplinary cross-integration.

As a symbol of urban culture, meaning and emotion, “scene” is a concept beyond the physical space.In the context of the knowledge economy, urban scene is an abstract concept describing culture, values and lifestyle generated by the combination of amenities.It is regarded to attract high-quality human capital and thus is the endogenous driving force of the economy and urban development.Therefore, accurately grasping the state and spatial distribution of urban scenes is an essential dimension of urban development.Several studies have mapped urban scenes based on the scale of the whole city or region, such as ZIP code tabulation area via official commercial codes or Dianping data.This study attempts to propose a methodological framework to achieve fine-grained mapping of urban scenes based on POI data and statistical methods.Scenes in Shenzhen are estimated, and the results show that the main scenes of Shenzhen are corporate, formality, exhibitionism, fashion and transgression.Moreover, three scenes patterns are presented, which may come from work, residential and creative entertainment spaces, respectively.In general, a practical methodological framework is proposed to map finer-grained scenes in cities, which is conducive to a more profound understanding and accurate identification of urban scenes and brings inspiration for urban development.

Large-scale agent-based social simulation is gradually proved to be an effective method for the study of human society.It can contribute to decision-making in social science, distributed artificial intelligence and agent technology in computer science, theory and modeling practice of computer simulation system, etc.However, the existing research practice has difficulties in balancing model complexity and simulation performance.In view of the existing problems, this paper proposes a conceptual model framework of large-scale social simulation based on agent and big data driving, and provides the reference implementation of mo-del components.Taking the epidemic prediction and control in a large-scale artificial city as an example, it illustrates how to use the proposed conceptual framework to model the large-scale social system with complex human behavior and social interaction.It also points out the potential applications in other social science fields, such as micro transportation system and urban evacuation planning.

Complex social system modeling is the principle problem in social computing.Considering the modeling process and requirements in the field of social computing, a model deep integration architecture called POV framework is proposed.The framework consists of three parts, physical layer, overlay layer and virtual layer, which provides the method of model organization, expression and integration.Based on this method, an interactive sharing and integration platform for social computing data model is built, which provides researchers with a social computing experimental platform including data resources, analysis tools, modeling and simulation computing environment.Application examples show that the platform can provide effective support for researchers to carry out social computing research.

With the rapid development of human-computer interaction in computer aided field, EEG has become the main means of emotion recognition.Meanwhile, graph network has attracted wide attention due to its excellent ability to represent topological data.To further improve the representation performance of graph network on multi-channel EEG signals, in this paper, conside-ring the sparsity and infrequency of EEG signals, a self-adaptive brain graph convolutional network with spatiotemporal attention (SABGCN-ST) is proposed.The method solves the sparsity of emotion via the spatiotemporal attention mechanism and explores the functional connections between different electrode channels via the self-adaptive brain network topological adjacent matrix.Finally, the feature learning of graph structure is operated via graph convolution, and the emotion is predicted.Extensive experiments conduct on two benchmark datasets DEAP and SEED prove that SABGCN-ST has a significant advantage in accuracy compared with baseline models, and the average accuracy of SABGCN-ST reaches 84.91%.

On the crowdsourcing platform, different types of users have diversity and differences in participation intention, work motivation, business ability and other aspects, and the value they generated on the platform is also different.The segmentation of users based on user value measurement is the key to better insight into user value and needs for personalized and refined management of users.At the same time, the choice of crowdsourcing user value measurement dimension is also a problem to be solved.Therefore, based on the RFM model, combined with the characteristics of crowdsourcing platform and crowdsourcing users, this paper firstly incorporates user credit into the user value model, proposes and constructes a crowdsourcing user value measurement model-RFMC.Secondly, combined with the required data obtained on the platform of “Yipinweike”, using GBDT algorithm to complete the crowdsourcing user classification.Finally, the classification performance of Nave Bayes, Multinomial Logistic Regression and GBDT are compared.Also, the classification performance of RFMC model is compared with that of traditional model without considering user credit.Evaluation indicators show that the proposed model is suitable for crowdsourcing users and has good experimental results.

Link prediction is an important task in network science.It aims to predict the link existence probabilities of two nodes.There are many relations between substances in real word, which can be described by network science in computers.There are many problems of daily life, which can be transformed to link prediction tasks.Link prediction algorithms for node featureless networks are convenient to migrate in directed networks, weighted networks, time networks, and so on.However, the traditional link prediction algorithms are faced with many problems as follows.The network structures information mining is not deep enough.The feature extraction processes depend on subjective consciousness.The algorithms are short of universality, and the time complexity and space complexity are flawed, which cause that they are difficult to be applied to real industry networks.In order to effectively avoid the above problems, based on the basic structure of graph attention network, graph embedding representation technology is used to collect node characteristics, analogy with the memory addressing strategy in neural turing machine, and combined with the relevant work of important node discovery in complex network, a fast and efficient attention calculation method is designed, and a node featureless network link prediction algorithm FALP integrating fast attention mechanism is proposed.Experiment on three public datasets and a private dataset show that the FALP effectively avoids these problems and has excellent predictive performance.

Aiming at the problem of community discovery in relational networks, considering the strength of interaction between nodes and information seepage mechanism, an edge weight and connected component (EWCC) community discovery algorithm based on edge weight and connected branches is innovatively proposed.In order to verify effectiveness of the algorithm, firstly, five kinds of interactive two-layer network models are constructed.By analyzing influence of interaction degree of nodes between layers on the network topology, 30 data sets generated under five kinds of two-layer network models are determined.Secondly, the real data set is selected to compare with GN algorithm and KL algorithm in the evaluation criteria of modularity, algorithm complexity and community division number.Experimental results show that EWCC algorithm has high accuracy.Then, the numerical simulation shows that with the weakening of interaction relationship between layers, the module degree is inversely proportional to number of communities, and the community division effect is better when node relationship between layers is weaker.Finally, as an application of the algorithm, the “user-APP” two-layer network is constructed based on empirical data, and the community is divided.

In the era of mobile Internet, it has become an irreversible trend that the social relationship chain goes online.The appearance of WeChat official account not only improves the convenience of information acquisition, but also increases the difficulty of system information governance.The research about the dissemination process of official account information on the WeChat social network and curbing the spread of rumors on social networks become the focus of WeChat operators and social regulator authorities.Based on the SEIR infectious disease model, this paper uses the real operating data provided by Beijing Sootoo Company to calculate and simulate the mutual conversion probability of the S-state, E-state, I-state and R-state users, and restores the whole link process of official account information dissemination on WeChat social network.In addition, this paper also quantitatively analyzes the influence of the number of official account fans, the influence of fans, the infection probability P₁ of susceptible users into exposed users, and the dissemination probability P₂ of exposed users into infected users on the process of information dissemination, which proves the effectiveness of the key opinion leader's forced immunization strategy in suppressing information dissemination.

Government big data is the core asset for digital government construction in the new era, and it is of great significance to the upgrade of government functions and services and the development of economic and social innovation.However, in a complex network circulation environment, in order to ensure the rational, orderly, and reliable use of government big data, capability building for data security protection cannot be ignored.On the technical aspect, government big data security protection involves several core elements, including the network security, platform security, and application security, and on the management aspect, government big data security protection needs to be focused on personnel quality and institutional quality.On the basis of the oretical discussions, specific technical and management capability indicators are given, and the construction practice of provincial-level agency unit A is analyzed.

With the rapid development of artificial intelligence (AI) technology, the application of AI-related technologies in law is increasedand attracts extensive attention.Specifically, AI has emerged in multiple legal scenarios such as automatic contract review and smart courts, compared with traditional artificial intelligence, its high efficiency shows its great application potential in the judicial field.However, in other scenarios such as legal judgement prediction (LJP), AI faces challenges and doubts in data analysis and algorithms, although some attempts have been made.Through analysis of the work related to legal AI, this paper summarizes the potential problems in datasets and algorithms in intelligent referees, investigates the changes in judicial progress that AI may bring and discusses whether the problems encountered by AI will affect the justice of law.Finally, this paper briefly expresses the potential solutions to the above problems, and provides insights into its future development, in the hope that AI technology will have a more systematic application in China's judicial field and contributeto the construction of socialist rule of law.

Socioeconomic Status (SES), an overall measure of a person's economic and social status relative to others combining factors such as economics and sociology, has received a lot of attention from researchers, as its assessment can help relevant orga-nizations to make various policies and decisions (governmental formulation of social policies, advertising personalized services, etc).In addition, with the development of big data technology and machine learning in recent years, assessing people's socioeconomic attributes (SEAs) and further obtaining the corresponding socioeconomic status with a data-driven approach can address the issue of extremely high cost of traditional methods.Therefore, this paper summarizes the research progresses of applying big data techniques to socioeconomic status analysis in recent years.It first introduces the basic concept of socioeconomic status and discusses the challenges posed by big data methods compared to traditional methods.After that, it systematically summarizes and classifies the state-of-the-art related methods based on the information in the learning process, and present them in detail, discusses the pros and cons of each type of method.Finally, it discusses the challenges and problems of inferring people's socioeconomic status and provides an outlook on future research directions.