Rise and decline process of online communities : modeling social balance of participants

Some online communities like Friendster had declined, and some of the others are said to be declining. Recent research has revealed the mechanism of decline as well as that of rise in each community. However, no comprehensive research has yet revealed the difference in declining mechanisms of each communities. We considered the online communities as networks of users and topics and defined behavior of users using Heider's balance theory. Users in our model are in a dilemma, stuck between topic preference and the balance between neighboring users. How the user behaves in the dilemma, his/her strategy, disseminates to other users. We simulate online communities using the model and observe the rise and decline of different kinds of communities. As a result, we found that two types of communities tend to develop with many users: communities in which the topic changes dynamically (FreeTopic-type) and communities in which the topic changes gradually (Topic-type). However, the property of each community and behavior of users are different. We found by simulation that the collaborative behavior of users happens very frequently in the FreeTopictype community, in which users consider the balance between each other rather than their topic preference. As a result, the FreeTopic-type communities do not often crash (i.e. quickly lose users). In addition, we confirmed that the postings about a topic are either negative or positive in the FreeTopic-type community. On the other hand, in the Topic-type community, simulation results indicate that users prioritize their preference for a topic. This causes the community to crash very frequently. However, users in such a community are found to obtain more benefits than in FreeTopic-type communities. It can be said that, after crashes occur, the community is still relatively beneficial for some users who remain

個人の適応行動に基づくオンラインコミュニティのダイナミクスの研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 大橋 弘忠, 東京大学教授 古田 一雄, 東京大学教授 奥田 洋司, 東京大学准教授 白山 晋, 東京大学准教授 和泉 潔, 東京大学准教授 鳥海 不二夫University of Tokyo(東京大学

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc

Rise and decline process of online communities : modeling social balance of participants

Some online communities like Friendster had declined, and some of the others are said to be declining. Recent research has revealed the mechanism of decline as well as that of rise in each community. However, no comprehensive research has yet revealed the difference in declining mechanisms of each communities. We considered the online communities as networks of users and topics and defined behavior of users using Heider's balance theory. Users in our model are in a dilemma, stuck between topic preference and the balance between neighboring users. How the user behaves in the dilemma, his/her strategy, disseminates to other users. We simulate online communities using the model and observe the rise and decline of different kinds of communities. As a result, we found that two types of communities tend to develop with many users: communities in which the topic changes dynamically (FreeTopic-type) and communities in which the topic changes gradually (Topic-type). However, the property of each community and behavior of users are different. We found by simulation that the collaborative behavior of users happens very frequently in the FreeTopictype community, in which users consider the balance between each other rather than their topic preference. As a result, the FreeTopic-type communities do not often crash (i.e. quickly lose users). In addition, we confirmed that the postings about a topic are either negative or positive in the FreeTopic-type community. On the other hand, in the Topic-type community, simulation results indicate that users prioritize their preference for a topic. This causes the community to crash very frequently. However, users in such a community are found to obtain more benefits than in FreeTopic-type communities. It can be said that, after crashes occur, the community is still relatively beneficial for some users who remain

Scientific Attention to Sustainability and SDGs: Meta-Analysis of Academic Papers

Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science’s diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as “inclusive society” and “early childhood development”, by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs

ROC curves for WIC with each training period.

Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials.</div

The 100 material words in each material class (listed in descending order of occurrence number).

The 100 material words in each material class (listed in descending order of occurrence number).</p

ROC curves for CNSH with each training period.

Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials.</div

Percentage of the co-occurrence of the top 10 most frequently occurring material words.

Percentage of the co-occurrence of the top 10 most frequently occurring material words.</p