- Open Access
When social computing meets soft computing: opportunities and insights
© The Author(s) 2018
- Received: 30 January 2018
- Accepted: 12 March 2018
- Published: 26 March 2018
The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.
- Social computing
- Soft computing
- Fuzzy logic
- Formal concept analysis
- Rough sets
Social media are computer-mediated tools that allow people to create, share or exchange information, ideas, pictures, audio or videos in virtual communities by using open Internet. Among online social networking services, there exist very interesting and challengeable research works on how to improve an efficient social media computing and how to make an effective social network analysis and mining from the perspectives of both academia and industry. Therefore, social computing, as a research discipline, is emerging for handling those kind of data generated from social media. Normally, various social computing related techniques include statistical approaches, graph based approaches and so forth. However, a human nature is present in the social networks. This implies that the social networks are human-like-full of imprecise relations and connections between individuals, vague terms, groups and individuals with indefinite descriptions and characteristics of interests . In order to better cope with these burning issues, advances on soft computing technologies, such as fuzzy set, formal concept analysis and rough set theories, probabilistic computing, as well as neural network and system, are paving a road to more valuable and feasible solutions to the emerging social media and big data, finally bringing a brilliant future of wisdom and intelligent social media network. This survey will be carried out for SNA from following various aspects, i.e., network representation, reputation/position analysis of users, social relationships characterization, topological structure analysis, social data analysis.
This paper is structured as follows: “Social computing” section overviews the main stream soft computing techniques; Then, a comprehensive taxonomy and its soft computing techniques based SNA approaches are presented in “Soft computing” section. Finally, “When social computing meets soft computing” section concludes this paper by presenting general remarks regarding the current stage of the research and a brief analysis of future perspectives.
This section will present the basic definition of social computing as well as the potential applications.
Definition of social computing
The terminology of social computing was firstly proposed in 1994. However, there are various definitions for social computing. Schuler  pointed out that social computing can be any type of computing application that uses software as a medium or focus of social relations. Therefore, his opinion emphasized the importance of social softwares. Dryer et al.  stated that social computing is the interplay between persons, social behaviors, and interactions with computing technologies, its design model focuses on the reciprocal interaction of the system design, human behavior, social contribution and interaction results in the mobile computing system. Wang et al. [4, 5] provided the definition of social computing in the narrow/broad sense. Broadly speaking, social computing refers to the computational theory and method for social sciences. Narrowly speaking, social computing is a computational theory and method for social activities, social processes, social structures, social organizations and their functions and effects.
Research fields of social computing
Social computing is not only a technique but also a social phenomena. Basically, social computing has two main research trends: (1) social science-oriented social computing; (2) application-oriented social computing. And, these two research trends influence each other.
Social science-oriented social computing
Social science-oriented social computing is composed of social networks analysis and computational social science. First, social network analysis mainly covers the topics on social flows, healthcare, key nodes mining for disease dissemination, communities detection and so forth. The approaches about social network analysis are mainly categorized as: agent-based model, theoretical physics approach, and graph theory. Milgram et al.  and Watts et al.  pioneered the research on small-world. Based on their work, Barabasi et al.  found the connection between nodes followed the power-law distribution. In addition, there are other significant research achievements, such as strong and weak ties , structural holes , and information cascades , etc. Second, computational social science is a cross-discipline among systems science, control science, and complex science. It mainly focuses on the research on social simulation and social system modeling by using equation based modeling and computational modeling . Technically, data mining as a key technology for computational social science, is to discover the interesting and useful patterns from massive data by using machine learning approaches.
Application-oriented social computing
Application-oriented social computing refers to a type of particular application which incorporating the methodologies and technologies about social computing, such as communities, social network, social phycology. Application-oriented social computing experienced three phases: group software, social software and social media . Group software was proposed in 1970s, and it was used in many research institutes. The essence of group software is a collaborative technology with the aim of supporting the interactions collaboratively. For example, computer supported cooperative work and computer supported collaborative learning are two classic group software applications. In 2005, as the rapid development of Web 2.0 , social media is emerging. Social media emphasizes the active interaction from users, users can complete the social interactions by generating, consuming the contents over the social media. Recently, the wide usage of ubiquitous devices, such as mobile phones and smart devices, the mobile social media [15–17] attracts much attention from academia and industry.
Fuzzy logic (FL), a commonly used soft computing approach, provides a simple way to get a definite conclusion based upon vague, ambiguous, imprecise, noisy or missing information of inputs. The working principle of FL is to process the data by allowing partial set membership rather than crisp set membership or non-membership. Fuzzy expert system consists of fuzzification unit that converts crisp values into fuzzified input . It consists of inference engine that contains if then else rules and a defuzzification unit to convert the result in a readable form. FL incorporates fuzzy rule based IF X AND Y THEN Z inference approach to solve problem rather than attempting to model a system mathematically. For example, fuzzy inference engine can be used for obtaining the trust relationship between mobile users in mobile social networks .
Formal concept analysis
Formal concept analysis (FCA)  is a typical computational intelligence technique for data analysis. FCA defines formal concepts to represent the relationships between objects and attributes in a domain. The objects and attributes are grouped into formal concepts, and then a conceptual hierarchy of all formal concepts (also called as formal concept lattice) can be constructed, which is a complete lattice. Therefore, giving a formal context, FCA can derive all formal concepts from this context and construct their formal concept lattice. Formally, relations of subsets of objects as well as attributes can be analyzed in the formal concept lattice, in addition, conceptual hierarchy provide information to order them according to a subconcept–superconcept relation [28, 29].
Rough set theory
Rough sets theory (RST) [30–32] has been widely used for processing the incomplete and uncertain information. Recent years have witnessed the ubiquitous applications of RST in machine learning, data mining, and decision support analysis fields. Theoretically, RST provides a useful method to understand unkownledge by using knowledge base, in fact, when the available information is not enough to determine the exact value of a given set, lower and upper approximations in RST can be used for the representation of this set. The approximation synthesis of concepts from the acquired data is the main objective of the rough set analysis . In real world practices, if a subset is difficult to define a concept in a given knowledge base, then rough sets can “approximate” the subset with respect to the knowledge base.
Soft set theory
The soft set theory [34, 35] is used as a general mathematical tool for dealing with uncertainty. It is proved that soft set is the generalization of the fuzzy set , also a topological space \((X, \tau )\) can be represented by soft set. Up to now, many operations and applications of soft sets have been provided [37, 38]. Soft set, as efficient uncertain information processing mathematical methodology, is used to help us for finding the optimized solution under the uncertain environment. It can easily characterize the incomplete and uncertain information from the parameterization point of view, especially for the inconsistency and incompleteness of the uncertain information.
As the scale of social media and number of users are rapidly increasing, social network analysis (SNA) has become an important tool for experts and researchers in social computing. Specially, the necessary information is often distributed and hidden on social site servers, so there is an urgent demand for designing some new approaches for collection and analysis the social network. Soft computing methodologies like fuzzy sets, neural networks, genetic algorithms, rough sets, soft sets and their hybridizations, have recently been widely utilized to solve data mining problems. They strive to provide approximate solutions at low cost, thereby speeding up the process. In this paper, we firstly answered what happening when social computing meets soft computing. Then, we discussed the state-of-art on various soft computing techniques those are used for social networks analysis. It is believed that this paper can provide more insights for researchers from the both social computing and soft computing fields.
Equiconcepts refer to a type of special formal concepts where the extent equals to the intent.
FH collected, reviewed and classified main literature for the paper, and also completed the writing of this work. DSP identified the insights of this work, especially the soft computing based social computing techniques in ubiquitous healthcare. ZP improved the part of soft computing techniques survey and presentation. All authors read and approved the final manuscript.
This research was supported by the National Natural Science Foundation of China (Grant Nos. 61702317, 61372187) and MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2014-0-00720) supervised by the IITP (Institute for Information & Communications Technology Promotion) and the National Research Foundation of Korea (No. NRF-2017R1A2B1008421) and was also supported by the Fundamental Research Funds for the Central Universities (GK201703059, GK201802013), and Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Province (Grant No. 2017024).
The authors declare that they have no competing interests.
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