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Abstract Social structures and interpersonal relationships may be represented as social networks consisting of nodes corresponding to people and links between pairs of nodes corresponding to relationships between those people. Social networks can be constructed by examining actual groups of people and identifying the relationships of interest betwe...
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... that in mind, the algorithms described here both make use of inferred personality types for the people represented by the network's nodes and base the probability of a link forming between two nodes on the compatibility of the personality types associated with those nodes. Table 4 is such a personality compatibility table for the MBTI personality types. The rows and columns are the 16 MBTI personality types. ...Context 2
... that the table is symmetric, i.e., the two entries for two personality types are the same regardless of which type is on the row and the column. Table 4 was constructed from the personality type descriptions in (Keirsey 1998); the process for doing so is detailed in Appendix 1. ...Context 3
... and heterophily can be modeled as likelihoods of link formation among personality types. In Table 4, values on the diagonal of the table represent a level of homophily because cells on the diagonal are the intersections of rows and columns identifying the same personality type. Values in the cells other than the diagonal represent some level of heterophily because those cells are at the intersections of rows and columns that identify different personality types. ...Context 4
... networks from a personality type assignment Synthetic social networks are generated by an algorithm that considers personality compatibility by using a personality compatibility table C (e.g., Table 4) and a personality assignment A to the nodes of the network. The network generation algorithm is denoted the G(n, A, C) (GNAC) algorithm, where n is the number of nodes, A is an assignment of personality types to the n nodes, and C is a personality compatibility table that includes the personality types in A. Given an assignment A of personality types to nodes and a compatibility table C, as many synthetic social networks as needed can be generated using the GNAC algorithm. ...Similar publications
Language has central role on intellectual development, social, and emotional students to supported success student and learn all of lessons. Literacy level are performation, functional, informational, and epistemic. Performative level means students able to read, to write, to listen, and to speak using symbols. Reading habit refers to the automatic...
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... It is accomplished by including the randomness factor in the model to make nodes act like real-world social network users. Real-world social network users depend on plenty of random factors [20] such as mood, fatigue level, engagement in social network activity, etc. Therefore, it is essential to consider such factors when modeling the information dissemination process. ...
This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network's status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces.
In recent years, Chinese Generation Z has shown a strong enthusiasm for the Myers-Briggs Type Indicator (MBTI), often attributing academic or life challenges to their MBTI personality types. This study aims to explore the effects of MBTI on academic major selection, academic performance, and career decision-making among first-year university students in China. Data were collected from 203 freshmen across seven majors at a comprehensive university in Guangdong Province using MBTI personality test scales, peer evaluations, and an open-ended career decision questionnaire. Statistical and correlation analyses were conducted using SPSS 27. The findings revealed that: (1) the Judging/Perceiving dimension is significantly correlated with structured disciplines; (2) Intuitive students tend to perform better academically; and (3) MBTI has a limited impact on career decision-making. These results suggest that while MBTI can offer insights into student preferences, its predictive power is constrained, especially in culturally and societally influenced contexts. Educators should take these factors into account when utilizing MBTI in academic settings and encourage students to explore career paths beyond their perceived personality constraints.
Social network analysis is a powerful tool for understanding various phenomena, but it requires data with explicit connections among users. However, such data is hard to obtain in real-time, especially from platforms like X, commonly known as Twitter, where users share topic-related content rather than personal connections. Therefore, this paper tackles a new problem of building a social network graph in real-time where explicit connections are unavailable. Our methodology is centred around the concept of user similarity as the fundamental basis for establishing connections, suggesting that users with similar characteristics are more likely to form connections. To implement this concept, we extracted easily accessible attributes from the Twitter platform and proposed a novel graph model based on similarity. We also introduce an Attribute-Weighted Euclidean Distance (AWED) to calculate user similarities. We compare the proposed graph with synthetic graphs based on network properties, online social network characteristics, and predictive analysis. The results suggest that the AWED graph provides a more precise representation of the dynamic connections that exist in real-world online social networks, surpassing the inherent constraints of synthetic graphs. We demonstrate that the proposed method of graph construction is simple, flexible, and effective for network analysis tasks.
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency. However, traditional methods such as Emotional Support Conversations (ESC) face challenges in effectively addressing a diverse range of individual personalities. In response, we introduce the Social Support Conversation (S2Conv) framework. It comprises a series of support agents and the interpersonal matching mechanism, linking individuals with persona-compatible virtual supporters. Utilizing persona decomposition based on the MBTI (Myers-Briggs Type Indicator), we have created the MBTI-1024 Bank, a group that of virtual characters with distinct profiles. Through improved role-playing prompts with behavior preset and dynamic memory, we facilitate the development of the MBTI-S2Conv dataset, which contains conversations between the characters in the MBTI-1024 Bank. Building upon these foundations, we present CharacterChat, a comprehensive S2Conv system, which includes a conversational model driven by personas and memories, along with an interpersonal matching plugin model that dispatches the optimal supporters from the MBTI-1024 Bank for individuals with specific personas. Empirical results indicate the remarkable efficacy of CharacterChat in providing personalized social support and highlight the substantial advantages derived from interpersonal matching. The source code is available in \url{https://github.com/morecry/CharacterChat}.
