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1 ( )
2 MixVis MixVis ( ) MixVis MixVis MixVis
3 (SNA) SNA Linking & Brushing i
4 MixVis: SNA ii
5 Linking & Brushing MixVis iii
6 t iv
7 1 1.1 ( ) G = (V, E) V E web2.0 SNS mixi (mixi.inc. ) MySpace SNS 1.2 (SNA) (Centrality) (Shortest Path)
8 [26] [14] N N ( )v w ( ) v w v w w v (v, w) (w, v) (v, w) (w, v) [23, 38, 5] (visual images) [16] A, B, C, D, E, F G, H, I, J, K C G 2
9 1.1: A B C D E F G H I J K A B C D E F G H I J K :
10
11 2 SNA 2.1 Freeman [8] For the most part, they (SNA) seek to uncover two kinds of patterns: (1) those that reveal subsets of actors that are organized into cohesive social groups, and (2) those that reveal subsets of actors that occupy equivalent social positions, or roles. Scott Wasserman [34, 35] SNA (centrality) ( ) centrality (Degree Centrality) 5
12 1.1, A 5 (Betweenness Centrality) A B C D E F 1 G H I J K C G C G SNA (relational data) mixi [40] Henry NodeTrix InfoVis cross pattern block pattern 2.2 SNA 6
13 [21, 1] 7
14 3 Linking & Brushing SNA 8
15 tagcloud) ( 3.1 ) flickr : ( ) Newman[28] [31, 20] [22] 1 9
![[18] 4 3.](/docs-images/95/124573388/images/16-0.jpg "2: 3.2.3 3.")
16 [18] 4 3.2:
![3.3: SocialAction[31, 33, 32] 3.](/docs-images/95/124573388/images/17-0.jpg "3 Linking & Brushing Linking & Brushing 1 Linking Brushing Linking Brushing Linking")
17 3.3: SocialAction[31, 33, 32] 3.3 Linking & Brushing Linking & Brushing 1 Linking Brushing Linking Brushing Linking & Brushing Linking & Brushing Linking & Brushing 11
18 3.4: Linking & Brushing Linking & Brushing Linking & Brushing 3.4 Linking & Brushing 12
19 node-link a b c 4.1: [37, 36, 30, 13] ( 4.2a,b ) 13
20 [18] 4.2c 4.2: 4.3 [36] 4.3: 4.2 G G c = (V, F ) G a = (V, A) V, F A 14
21 G =(V, F, A), V ={v 1, v 2, v 3,, v n } F {(m, n) m, n V, m n} A {{u, v} u, v V, u v} (directed) 4.3 i ii iii 15
22 4.4 [11] F A E G =(V, E) (4.1) E =A {{u, v} (u, v) F } (4.2) G f s = C s (l d) 1 d (4.3) d l C s 4.4 C r 1 d d L 0 f r = (4.4) 0 d > L 0 C r L
23 4.1: cluster leaf cluster l 1 l 2 sister otherwise leaf l 2 l 3 l 4 4.2: leaf cluster cluster l 5 l (l 1, l 2, l 6 ) > (l 4, l 5 ) > l 3 (4.5) d d L N L 4.4: 17
24 f r : Graham Scan[19] ( ) 18
25 ( ) 19
26 第 5 章 MixVis:ソーシャルネットワーク分析の ためのツール 本章では 提案手法を元に開発した ソーシャルネットワーク分析のためのツール MixVis について述べる まず MixVis の概要の説明を行う その後 インタフェースの設計につい て詳しく述べる 最後に本ツールを実現するためのシステム構成を紹介する 5.1 インタフェースの概要 図 5.1: ツールの概観 提案手法に基づいて ソーシャルネットワーク分析のための視覚的ツール MixVis を開発し た 図 5.1 のように MixVis は主に３つのビューで構成される ソーシャルネットワークに関 20
27 5.2 MixVis Brandes [7] ( 5.2 ) 5.3 Drag & Drop 21
28 5.2: 5.3: Reset Shake Stop Default 22
29 5.2.2 Sort by alphabet Sort by Degree Centrality Sort by Betweenness Centrality ( 5.4 ) 5.4: : Ctrl+Click Shift+Click HSV 23
30 :
31 図 5.7: ネットワーク図から各ビューへの連携 図 5.8 は ひとつのタグをクリックした後の MixVis のスナップショットである 分 析者が特定のアクター たとえば 名前が 筑波太郎 や次数中心性上位３人や媒介中 心性最も低いアクターなど に興味を持ち そのアクターがどのようなアクターにつな がっているか どのようなコミュニティに属しているかを調べるときに この図のよう に MixVis は分析者の作業を支援する リストから各ビューの連携 リストのアイテムが選択されると そのコミュニティのメンバーを囲む領域がアイ テムと同じ色でハイライトされる メンバーを表すタグもその色でハイライトされる 複数のコミュニティに属しているアクターのタグは それらのコミュニティを混ぜ合わ せた色で表示される 図 5.9 は２つのコミュニティを選択した後の各ビューの様子を示 している リストでは 選択した２つのコミュニティの背景色が茶色と赤色となった ネット ワーク図で対応したコミュニティが 同じ色で透過性のある領域で描かれた ここで 複合グラフの描画手法におけるコミュニティの描画によって コミュニティのメンバー がその領域に囲まれ 共有されるメンバーが領域のインタセクションに配置される そ して コミュニティに入っていないアクターは できるだけ領域から取り除かれるよう 25
32 5.8: MixVis Java TM Platform, Standard Edition 6 Development Kit 1 MixVis Java TM Applet GraphML GraphML XML [6]
33 図 5.9: リストからの各ビューの連携 5.4 システム構成 図 5.10 は MixVis のシステム構成を示す アクター つながり そしてとコミュニティの情報はリレーショナルデータベースに保存さ れている そのデータベースから 描画するデータを抽出して GraphML のフォーマットで 編成する システムは パーサーを用いて GraphML データを解析する その結果から ノー ド エッジ そしてクラスタを取得して処理する ノードはタグにされ タグクラウドとして表現される エッジを含むネットワーク全体は 複合グラフ描画手法を用いて描かれる クラスタはさらにリストで提示される 27
34 5.10: MixVis 28
35 6 MixVis SNS 6.1 (SNS) SNS SNS GraphML 150 [10]
36 ( ) Freeman MixVis : 30
37 7 7.1 SNA 7.2 ToolA ToolB ToolA ToolB ToolA: MixVis ( ) ToolA SNA ToolB: ToolB MixVis ToolB 31
38 ToolA ToolB MixVis ToolC ToolA ToolB ToolC ToolA ToolB ToolC(MixVis) 7 1: 3 2:
39 3: 4: 5: 6 : 7: (1 5 ) MixVis 2 33
40 点数の平均値 ToolA ToolB MixVis タスク 7.1: 7.1: t ToolA MixVis t ToolB MixVis t t t t 5% t 2.26 ( 9) ToolB MixVis ToolA 2 ToolB, 3 MixVis 6 ToolB MixVis 5 7 MixVis t 34
41 2 ToolA ToolB 7.2 ToolA ToolB MixVis 7.2: MixVis 35
42 8 8.1 SNA UCINET[5] Pajek[9] UCINET Pajek[3] Pajek StOCNET[4] 6 StOCNET06 node-link NetDraw UCINET Pajek StOCNET Pajek Heer Vizter[20] friendster 1 tribe 2 orkut 3 SNS Vizster PivotGraph ( ) [39]
43 PivotGraph Henry Node-link MatrixExplorer [21] Node-link NodeTrix, Node-link [22] Node-link NddeTrix SocialAction Perer [31, 33, 32] SocialAction node-link node-link NetLens[24] NetLens TreePlus[25] TreePlus 8.2 Misue & Sugiyama[27, 37] Omote & Sugiyama[29, 30] Intersecting Compound Mixed Graph(ICMG) Frishman [17] Eades [12] orthogonal-grid rectangular cluster drawings (Planar drawings) Feng[15] 37
44 Balzer [2] 2D 3D levelof-detail 38
45 9 9.1 MixVis MixVis MixVis 9.2 MixVis ( ) MixVis 3 39
46 (IPLAB) 40
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50 [39] Martin Wattenberg. Visual exploration of multivariate graphs. In CHI 06: Proceedings of the SIGCHI conference on Human Factors in computing systems, pp , New York, NY, USA, ACM. [40],. SNS mixi., Vol. 22, No. 5,
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Mohammad Reza SARKAR ARANI Associate Professor, Allameh Tabatabai University Visiting Research Scholar, International Research Center for Japanese Studies 200718 Mohammad Reza SARKAR ARANI Visiting Research