| Presentation | 2024-03-13 Evaluating the Ranking Performance of Transferable Influencer Identification Methods with a Focus on Follower Counts of Influencers Kota Tahara, Sho Tsugawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Identifying influencers on social media who can spread information to many other users is one of the important research issues. We have investigated how accurately influencers can be predicted when the source and target domains of learning are different. However, our previous studies focused on whether the user is an influencer or not. On the other hand, there are differences in the strength of influence among influencers. The influence of the influencers identified by the prediction model has not been evaluated. Therefore, in this paper, we formulate the influencer identification problem as a ranking problem. In particular, we evaluate the effectiveness of influencer rankings obtained from the influencer prediction models when the source and target domains of learning are different. The results show that influencer prediction model based on graph neural network is effective in identifying non-obvious influencers, such as influencers with not many followers but large influence. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | social graph / influencer / domain adaptation / graph neural network |
| Paper # | CQ2023-76 |
| Date of Issue | 2024-03-06 (CQ) |
| Conference Information | |
| Committee | IE / MVE / CQ / IMQ |
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| Conference Date | 2024/3/13(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Sangyo Shien Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） |
| Chair | Hiroyuki Bandoh(NTT) / Kiyoshi Kiyokawa(NAIST) / Takefumi Hiraguri(Nippon Inst. of Tech.) / Hiroaki Kudo(Nagoya Univ.) |
| Vice Chair | Yuichi Tanaka(Osaka Univ.) / Toshihiko Yamazaki(Univ. of Tokyo) / Sumaru Niida(KDDI Research) / Takahiro Matsuda(Tokyo Metropolitan Univ.) / Gou Hasegawa(Tohoku Univ.) / Sumaru Niida(KDDI Research) / Gosuke Ohashi(Shizuka Univ.) |
| Secretary | Yuichi Tanaka(NHK) / Toshihiko Yamazaki(Tottori Univ.) / Sumaru Niida(Otsuma Women's Univ.) / Takahiro Matsuda(DNP) / Gou Hasegawa(NTT) / Sumaru Niida(NTT) / Gosuke Ohashi(Tama Univ.) |
| Assistant | Kazunori Uruma(Kogakuin Univ.) / Shinobu Kudo(KDDI Research) / Hidehiko Shishido(Univ. of Tsukuba) / Atsushi Nakazawa(Kyoto Univ.) / Naoya Tojo(KDDI Research) / Naoki Hagiyama(NTT) / Yuji Tatada(Univ. of Tokyo) / Ryo Nakamura(Fukuoka Univ.) / Toshiro Nakahira(NTT) / Kenta Tsukatsune(Okayama Univ. of Science) / Kuniharu Imai(Nagoya Univ.) / Takashi Yamazoe(Seikei Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Image Engineering / Technical Committee on Media Experience and Virtual Environment / Technical Committee on Communication Quality / Technical Committee on Image Media Quality |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Evaluating the Ranking Performance of Transferable Influencer Identification Methods with a Focus on Follower Counts of Influencers |
| Sub Title (in English) | |
| Keyword(1) | social graph |
| Keyword(2) | influencer |
| Keyword(3) | domain adaptation |
| Keyword(4) | graph neural network |
| 1st Author's Name | Kota Tahara |
| 1st Author's Affiliation | University of Tsukuba(UT) |
| 2nd Author's Name | Sho Tsugawa |
| 2nd Author's Affiliation | University of Tsukuba(UT) |
| Date | 2024-03-13 |
| Paper # | CQ2023-76 |
| Volume (vol) | vol.123 |
| Number (no) | CQ-431 |
| Page | pp.pp.32-37(CQ), |
| #Pages | 6 |
| Date of Issue | 2024-03-06 (CQ) |