著者

白井 嵩士 榊 剛史 鳥海 不二夫 篠田 孝祐 風間 一洋 野田 五十樹 沼尾 正行 栗原 聡 Shirai Takashi Sakaki Takeshi Toriumi Fujio Shinoda Kosuke Kazama Kazuhiro Noda Itsuki Numao Masayuki Kurihara Satoshi

雑誌

SIG-DOCMAS = SIG-DOCMAS

巻号頁・発行日

no.B102, 2012-03-11

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Twitter is a famous social networking service and has received attention recently.Twitter user have increased rapidly, and many users exchange information. When 2011 Tohokuearthquake and tsunami happened, people were able to obtain information from social networkingservice. Though Twitter played the important role, one of the problem of Twitter, a false rumordiffusion, was pointed out. In this research, we focus on a false rumor diffusion. We propose ainformation diffusion model based on SIR model, and discuss how to prevent a false rumor diffusion.

著者

金 正福 川村 秀憲 鈴木 恵二 Shofuku KIN Hidenori KAWAMURA Keiji SUZUKI

雑誌

SIG-DOCMAS = SIG-DOCMAS

巻号頁・発行日

no.B301, 2013-10-22

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Multi-Agent Simulation (MAS) is efficient for analysis of various social mechanisms. Recently,there are many studies on massive agent model to explain more complex social phenomena. Then,we aim for implementation of large scale simulation model using Repast HPC toolkit, a platformfor massive agent model. In this article, we build ”Schelling Segregation Model” for spatial modelusing geospatial data provided OpenStreetMap, an open source project creating a free editablemap. In this model, agents are located continuous space , not grid in original. When an agentis ”unhappy” and migrate to new location, it costs agents some simulation time depending ondistance between old location and new one. This article reports simulation results using Japanesecities and verification result about execution time.

著者

片岡哲也 猪口 明博 Kataoka Tetsuya Inokuchi Akihiro

雑誌

SIG-DOCMAS = SIG-DOCMAS

巻号頁・発行日

vol.9, no.1, pp.1-7, 2015-02-27

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In this paper, we propose a novel graph kernel based on Hadamard cords. Our idea is based on the Walsh-Hadamard matrix and uneven division of xed-length bit string that expresses each vertex label. By updating all vertex labels in graphs iteratively with their adjacent vertices, each vertex is expressed as the bit string including features of its vertex label and vertex labels within h steps from the vertex. The time complexity of the proposed graph kernel is linear in the number of vertices of graphs and the length of the bit string. According to experiments we have conducted, our kernel outperforms state-of-the-art graph kernels with respect to both scalability and expressiveness.

著者

村上 知子 瀬戸口 久雄 鳥居 健太郎 内平 直志 Murakami Tomoko Setoguchi Hisao Torii Kentaro Uchihira Naoshi

雑誌

SIG-DOCMAS = SIG-DOCMAS

巻号頁・発行日

no.B102, 2012-03-11

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In this paper we propose a method to estimate working activities from sensor datawithout user annotation. Estimation of working activities are much more difficult than that ofsimple activities such as ’walking’ and ’still’ in terms of their complexity and length. To addressthis issue, we assume that they are a probabilistic combination of various simple activities andpropose a method to discover working activities by multistage estimation, in which firstly classifysensor data into some basic activities and then estimate them by using topic model.We focusedon nursing service as one of professional working activities, in whicn visualization by estimation ofworking activities is critical, conducted the experiment to observe it in the hospital and verifiedour method.

著者

神野良太 上原邦昭 JINNO Ryota UEHARA Kuniaki

雑誌

SIG-DOCMAS = SIG-DOCMAS

巻号頁・発行日

no.B101, 2011-12-14

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As the location-acquisition technologies become increasingly pervasive, tracking themovement of objects from trajectory datasets are more and more available. As a result, discoveringfrequent movement patterns from such a dataset has recently gained great interest. However,trajectory dataset is usually large in volume and exceeds the computation capacity of traditionalcentralized technologies. We propose a new approach to discovering patterns over a massive dataset based on distributed storage and computing. We apply the proposed approach to differentreal-world datasets in different conditions. We also discuss the results and possible future researchdirections.