研究ノート
看護系論文の共著者ネットワークの分析による
看護学専門領域の分類
今
井
哲
郎
＊・大
石
朋
子
＊・川
口
孝
泰
＊ 要旨：研究者コミュニティにおける研究活動を可視化するための手段の一つとして，学術論文の共 著者ネットワークを分析する手法が知られている．本稿では日本の看護系分野の研究に焦点を当 て，医中誌データベースから看護系論文の共著者ネットワークを作成，ネットワーク科学の観点か らコミュニティ検出をすることで，看護系分野の研究の専門領域を分類することを試みた．検出さ れたコミュニティの中には，専門領域を牽引するリーダーによって形成された独立性の高い学派に 対応するものがあり，研究者コミュニティにおける学派が，共著者ネットワークの構造のみから検 出できることが示された． キーワード：ネットワーク科学，コミュニティ検出，ビッグデータ，看護研究Classification of Nursing Research’
s Special Fields
From a Co-author Network of Nursing Research Articles
Tetsuo IMAI
＊, Tomoko OISHI
＊and Takayasu KAWAGUCHI
＊Abstract:Analysis of co-author networks of academic articles is known as a method to visualize activities
in researchers’ communities. In this article, which focuses on nursing research in Japan, a co-author network
of nursing articles is constructed from ICHUSHI database; and network communities are utilized to characterize the disciplines of nursing research. It is shown that some network communities present highly leader-dependent characteristics that vary from one discipline to another, and that set these communities clearly apart from the main-stream.
Keywords: Network science, Community detection, Big data, Nursing research
＊
東京情報大学 看護学部 遠隔看護実践研究センター 2017年9月19日受付 Telenursing Research Center, Faculty of Nursing, Tokyo University of Information Sciences 2018年1月17日受理
౦ژใେֶطଘͷ૯߹ใֶ෦ͱͷ2ֶ෦ମ੍ ͱͳΓɼڭҭ׆ಈͷΈͳΒͣݚڀ׆ಈʹ͓͍ͯɼ ޢͱใͷγφδʔޮՌʹΑΔ৽ͨͳՌ͕ظ ͞Ε͍ͯΔɽޢܥݚڀऀʹͱͬͯɼੜମηϯγ ϯάσόΠεΛ༻͍ͨԕִޢγεςϜͳͲͷΑ͏ ʹɼࠓޙͷޢֶʹใՊֶ͕ͲͷΑ͏ʹؔΘͬͯ ͘Δͷ͔Λҙࣝ͢Δػձଟ͋͘Γɼޢܥݚڀऀ ʹͱͬͯͷใֶɼطʹ͋ΔఔΠϝʔδ͢͠ ͍ͷʹͳ͍ͬͯΔɽҰํͰใܥݚڀऀʹͱͬͯ ɼใՊֶͷԠ༻ݚڀଟذʹΘͨΔͨΊʹɼ ޢֶͱ͍͏ͷʹೃછΈͷ͋ΔݚڀऀͦΕ΄Ͳଟ ͘ͳ͍ɽͦͷͨΊଟ͘ͷೃછΈͷͳ͍ݚڀऀʹͱͬ ͯɼޢֶʹ͍ͭͯ۩ମతͳΠϝʔδΛ͠ʹ͍͘ͱ ͍͏໘͕͋Δɽ ωοτϫʔΫՊֶɼใֶΛத৺ͱ͢Δֶࡍత ͳֶͰ͋ΔɽωοτϫʔΫՊֶͰɼੈͷத ͷ༷ʑͳࣄΛɼߏཁૉͱߏཁૉಉ࢜ͷ͔ؔ ΒΔωοτϫʔΫ(NW)ͱͯ͠ଊ͑ɼͦͷߏ ػೳʹ͍ͭͯݚڀ͢ΔɽNWՊֶʹ͓͍ͯɼݚ ڀΛ֓؍͢ΔͨΊͷϙϐϡϥʔͳख๏ͱͯ͠ɼ ڞஶNWΛੳ͢Δͱ͍͏ख๏͕͜Ε·Ͱʹ͘ ߦΘΕ͍ͯΔɽ͜Εݚڀऀಉ࢜ΛڞஶจͰ݁ͼ NWͱͯ͠ѻ͏͜ͱʹΑͬͯɼݚڀ͕࣋ͭશମ తͳݚڀऀಉ࢜ͷίϛϡχςΟΛચ͍ग़͢ख ๏Ͱ͋ΔɽຊߘͰɼޢܥݚڀʹ͓͚ΔڞஶNW Λੳ͠ɼಛʹݚڀऀಉ࢜ͷίϛϡχςΟΛੳ͢ Δ͜ͱͰɼݚڀऀؒͷίϥϘϨʔγϣϯʹΑͬͯ ࣗݾ৫తʹ(ϘτϜΞοϓతʹ)ܗ͞ΕΔઐ ྖҬͷநग़ΛࢼΈͨɽ͜ΕʹΑΓɼใܥݚڀऀʹ ͱͬͯɼޢܥݚڀʹର͢Δ၆ᛌతͳݟํΛಘΔ ͜ͱ͕ظͰ͖Δɽ·ͨޢܥݚڀऀʹͱͬͯɼ τοϓμϯతʹྨ͞Εͨ௨ৗͷઐྖҬʹ͍ͭ ͯɼઐྖҬؒͷؔ࿈ੑ͕ఆྔతʹࣔ͞ΕΔ͜ͱʹ Αͬͯɼࠓޙͷֶձͷ༗Γ༷ݚڀ׆ಈͷํੑʹ ؔ͢ΔࣔࠦΛಘΔ͜ͱ͕ظͰ͖Δɽ 2.1 ڞஶNWͷߏங ຊߘʹ͓͚ΔڞஶNWߏஙͷྲྀΕΛɼਤ1ʹࣔ ͢ɽຊߘʹ͓͚ΔڞஶNWɼஶऀΛϊʔυͱ͠ɼ2 ਓͷஶऀؒʹڞஶ͕ؔ͋Δ߹ʹϊʔυؒʹΤο δΛ༩ͯ͠ߏங͞ΕΔͷͰ͋ΔɽҰฤͷจʹ 3໊Ҏ্ͷஶऀ͕͋Δ߹ɼͦͷશͯͷஶऀؒʹ Τοδ͕༩͞ΕΔɽڞஶऀNWͷߏஙํ๏΄ ͔ʹɼڞஶϦετ্ͰྡΓ߹͏ஶऀͷΈʹΤοδ ΛுΔํ๏(ࣰా 2011; ਿࢁଞ 2006)[3][5]ɼॏ Έ͖ωοτϫʔΫͱͯ͠දݱ͢Δͷ(Newman 2001)[1]ɼڞஶจͷຊΤοδΛுΔํ๏ͳͲ ͕͋Δ͕ɼຊߘͰ࠷ϕʔγοΫͰϙϐϡϥʔͳ ํ๏Λ࠾༻ͨ͠ɽ จݙใσʔλϕʔεͱͯ͠ɼຊߘͰҩֶத ԝࡶࢽץߦձ͕ఏڙ͢Δҩதࢽσʔλϕʔε[1] Λ༻͍ͨɽҩதࢽσʔλϕʔεʹɼࠃൃߦͷҩ ֶɾࣃֶɾༀֶɾޢֶɾ৺ཧֶ͓Αͼؔ࿈ͷ ఆظץߦͷ6 000ࢽ͔Βऩͨ͠1 000ສ ݅ͷจใ͕ऩ͞Ε͓ͯΓɼʮࠃͷࡶࢽΛݕ ࡧ͢ΔͨΊʹ࠷ཏతͳใݯͰ͋Δʯ(দా 2009, p. 106)[4]ͱ͞ΕΔɽຊߘͷੳରޢ ܥݚڀͰ͋ΔͷͰɼநग़ରͱ͢ΔจݙΛɼޢܥ ֶձڠٞձձһͷ44ֶձ͕ൃߦ͢Δจࢽ(લ จࢽΛؚΉ)ʹ2015·Ͱʹܝࡌ͞Εͨݪஶ จ[2]ͱͨ͠ɽநग़͞ΕͨݪஶจͷΛɼද1 ʹࣔ͢ɽ ຊߘͰɼஶऀ໊ʹؔ͢Δ໊دͤͷॲཧΛ͍ͯ͠ ͳ͍ɽͦͷͨΊɼಉҰݚڀऀ͕݁ࠗʹΑ͕ͬͯ มΘͬͨͨΊʹෳͷݚڀऀͱͯ͠ѻΘΕΔέʔ εɼ·ͨٯʹɼಉಉ໊ͷҟͳΔݚڀऀ͕ಉҰݚڀ ऀͱͯ͠ѻΘΕͯ͠·͏έʔε͋Δͱߟ͑ΒΕ Δ͕ɼ͍ͣΕͷ߹ಛผͳॲཧΛ͍ͯ͠ͳ͍ɽຊ དྷɼঁੑݚڀऀ͕ଟ͍ޢֶʹ͓͍ͯɼଞͷ ʹൺͯ݁ࠗʹΑ͕ͬͯมΘΔݚڀऀ͕ଟ͍ͱݟ ΒΕɼվલޙͷஶऀ໊ʹ໊ؔͯ͠دͤΛ࣮ࢪ͢Δ ॏཁੑߴ͍ɽ͔͠͠ͳ͕Βɼจݙใσʔλϕʔ
ਤ1: ڞஶNWͷߏஙํ๏ ҩதࢽσʔλϕʔε͔ΒҰൠࣾஂ๏ਓຊޢܥֶձڠٞձձһֶձ44ֶձൃߦͷจࢽʹܝࡌ͞Εͨ ݪஶจΛநग़͠ɼஶऀΛϊʔυɼڞஶؔΛΤοδͱ͢Δ୯७άϥϑΛߏங͢Δɽ·֤ͨΤοδɼڞ ஶؔΛߏ͢Δ֤จࢽͷׂ߹Λࣔ͢44࣍ݩͷจࢽγΣΞϕΫτϧ(2.2અࢀর)Λ࣋ͭɽ ε͔Βػցతʹஶऀ໊ΛಘΔࠓճͷํ๏Ͱɼվ લޙͷݚڀऀΛඥ͚͢Δ͜ͱҰൠʹࠔͰ͋ Δ͜ͱ͔ΒɼຊߘͰ໊دͤॲཧΛࢪ͢͜ͱΛݟ ૹͬͨ[3]ɽ 2.2 จࢽγΣΞϕΫτϧ ຊߘͰɼஶऀؒͷڞஶ͕ؔͲͷจࢽʹΑΔ ͷ͔Λදͨ͢Ίʹɼ֤Τοδʹରͯ͠จࢽγΣ ΞϕΫτϧE Λ༩͢ΔɽจࢽγΣΞϕΫτϧ ɼͦͷΤοδ(ڞஶؔ)͕ɼͲͷจࢽͷจ ʹΑͬͯߏ͞Ε͍ͯΔ͔Λදͨ͠ͷͰ͋Δɽ۩ ମతʹɼΤοδeͷจࢽγΣΞϕΫτϧEe จࢽͷͱಉ࣍͡ݩͷϕΫτϧ(ຊߘͷ߹44 ࣍ݩ)Ͱද͞ΕɼEeͷ֤จࢽͷγΣΞ( ༗)Λࣔ͢ͱͳΔɽ֤ͷ૯ৗʹ1ͱ ͳΔɽྫ͑ɼஶऀB ͱஶऀC ͷؒʹڞஶจ ͕จࢽiͷจ2ຊͱจࢽj ͷจ2ຊͷΈ ͕͋Δ߹ɼΤοδBC ͷจࢽγΣΞϕΫτϧ EBC ɼୈiͱୈj͕ͱʹ2/4 = 0.5ɼ ΄͔ͷ0/4 = 0ͱͳΔɽ 2.3 ίϛϡχςΟݕग़ ޢܥݚڀʹ͓͚ΔݚڀऀίϛϡχςΟΛՄࢹԽ ͢ΔͨΊʹɼຊߘͰڞஶNWʹରͯ͠ίϛϡχ ςΟݕग़ΛߦͬͨɽίϛϡχςΟݕग़ͱNWʹ ͓͚ΔΫϥελϦϯάख๏ͷҰͭͰɼNWશମ͔Β ͭͳ͕Γͷڧ͍෦NW(ίϛϡχςΟ[4] )Λ ݕग़͢Δख๏Ͱ͋ΔɽίϛϡχςΟݕग़ΞϧΰϦζ Ϝͱͯ͠ɼຊߘͰGirvan–Newman๏(Newman and Girvan 2004)[2]Λ༻͍Δɽ͜ΕNWʹ͓ ͚ΔϊʔυΛϋʔυΫϥελϦϯά[5] ͢Δख ๏ͰɼNWՊֶʹ͓͚ΔίϛϡχςΟݕग़ͷख๏ ͱͯ͠ɼ࠷ϕʔγοΫͳͷͰ͋Δɽ
3
݁Ռͱߟ
3.1 ڞஶNWͷશମత 3.1.1 2015ͷڞஶNW 2015ͷڞஶNWશମͷϊʔυ9 478ɼΤο δ32 858Ͱ͋ͬͨɽڞஶNWશମɼҰͭͷ ڊେͳ࿈݁(ϝΠϯίϯϙʔωϯτ)ͱɼͦͷ ଞͷখ͞ͳنͷ࿈݁ʹ͔ΕΔɽϝΠϯίϯ ϙʔωϯτڞஶNW શମͷଟ͘ͷ෦ΛΊɼ ϊʔυͰ78%ɼΤοδͰ90%ʹͷ΅Δɽ NWՊֶʹ͓͍ͯɼϝΠϯίϯϙʔωϯτ(࠷େ ࿈݁)ʹணͯ͠ੳ͢Δ͜ͱ͕ଟ͍ɽຊߘͰ ɼҎԼͰϝΠϯίϯϙʔωϯτΛੳରͱ ͢Δɽ 3.1.2 ෳࡶNWͱͯ͠ ͜͜ͰɼෳࡶNWͷදతͳಛͰ͋Δεέʔ ϧϑϦʔੑͱεϞʔϧϫʔϧυੑʹ͍ͭͯௐΔɽ εέʔϧϑϦʔੑɼϊʔυͷ࣍(ϊʔυ͕࣋ͭ Τοδͷ)ʹؔ͢Δੑ࣭Ͱ͋Γɼେଟͷϊʔυ ͕Θ͔ͣͳ͔࣍࣋ͨ͠ͳ͍ҰํͰɼগͷϊʔυ ͕ඇৗʹେ͖ͳ࣍Λ࣋ͭͱ͍͏ੑ࣭Ͱ͋Δɽ௨ৗจࢽ໊ ݪஶจ (–2015) ຊޢՊֶձࢽ 1 149 ࿏Ճޢֶձࢽ 166 ຊ͕Μޢֶձࢽ 341 ຊޢֶڭҭֶձࢽ 288 ຊޢཧֶձࢽ 183 ຊޢݚڀֶձࡶࢽ 990 ຊٹٸޢֶձࡶࢽ 106 ຊΫϦςΟΧϧέΞޢֶձࢽ 94 ຊެऺӴੜޢֶձࢽ 44 ຊখࣇޢֶձࢽ 444 ຊॿ࢈ֶձࢽ 333 ຊਫ਼ਆอ݈ޢֶձࢽ 284 ຊইɾΦετϛʔɾࣦېཧֶձࢽ 187 ຊҬޢֶձࢽ 426 ຊපڭҭɾޢֶձࢽ 167 ຊੑޢֶձࢽ 137 ຊ॥ثޢֶձࢽ 73 ߴঁࢠେֶޢֶձࢽ 234 ઍ༿ޢֶձձࢽ 332 ΞσΟΫγϣϯޢ 41 ຊӡಈثޢֶձࢽ 54 Ոޢֶݚڀ 184 ຊޢҩྍֶձࡶࢽ 164 ຊޢٕज़ֶձࢽ 215 ޢڭҭֶݚڀ 102 ޢஅ 63 ຊޢࢱֶձࢽ 306 ຊޢྙཧֶձࢽ 60 ຊޢྺֶ࢙ձࢽ 29 ຊࡂޢֶձࢽ 109 ຊࡏέΞֶձࢽ 250 ຊखज़ޢֶձࢽ 182 ຊ৽ੜࣇޢֶձࢽ 119 ຊਛෆશޢֶձࢽ 135 ຊੜ৩ޢֶձࢽ 55 ຊेࣈޢֶձࢽ 121 ຊපޢֶձࢽ 152 ຊ์ࣹઢޢֶձࢽ 20 ຊࢠޢֶձࢽ 61 ຊຫੑޢֶձࢽ 30 ຊϧʔϥϧφʔγϯάֶձࢽ 62 ޢֶ 290 ຊޢֶձࢽ 135 ຊχϡʔϩαΠΤϯεޢֶձࢽ 18 ܭ 8 935 10-4 10-3 100 101 102 103 Probability (PDF) Node Degree ਤ2: ϝΠϯίϯϙʔωϯτͷ࣍Λ྆ର άϥϑͰࣔͨ͠ͷɽ ߴ࣍(࣍10Ҏ্)Ͱ͖ଇʹϑΟοτ͢Δ (ഁઢ͖γ = −2.6)͕ɼ࣍Ͱ͖ଇΑ Γখ͍͞ɽ ɼ͕͖࣍ʹ͕ͨ͠͏ͱ͖ɼͦͷNW εέʔϧϑϦʔੑΛ࣋ͭͱ͞ΕΔɽҰํͷεϞʔ ϧϫʔϧυੑ௨ৗɼฏۉϊʔυؒڑ͕NWن ʹରͯ͠ेখ͍͜͞ͱɼ·ͨΫϥελϦϯά (2ͭͷྡϊʔυؒʹΤοδ͕ଘࡏ͢Δׂ߹) ͕͋Δఔେ͖͍͜ͱɼͱ͍͏2ͭͷNWಛྔ ʹΑͬͯಛ͚ͮΒΕΔɽ͜Εɼզʑ͕ਓؒࣾ ձNWͰײ͡Δʮੈؒҙ֎ͱڱ͍(It’s a small world)ʯͱ͍͏2छྨͷ࣮ײΛɼNWͷ༻ޠͰද ݱͨ͠ͷͰ͋Δɽ ਤ2ʹɼϝΠϯίϯϙʔωϯτͷ࣍Λࣔ ͢ɽϝΠϯίϯϙʔωϯτͷ࣍ΛݟΔͱɼߴ ࣍(࣍10Ҏ্)Ͱ͖ଇʹϑΟοτ͢Δ(ഁ ઢ͖γ = −2.6)ͷͷɼ࣍Ͱ͖ଇΑ Γখ͘͞ɼεέʔϧϑϦʔੑऑ͍ɽ·ͨද2ʹ ϝΠϯίϯϙʔωϯτͷجૅతͳNWಛྔΛࣔ ͢ɽڞஶNWͷϝΠϯίϯϙʔωϯτɼϊʔυ ʹରͯ͠ेখ͞ͳฏۉϊʔυؒڑͱɼେ͖ͳ ΫϥελϦϯάΛ࣋ͭ͜ͱ͔ΒɼεϞʔϧϫʔ ϧυੑΛ༗͍ͯ͠Δ͜ͱ͕͔Δɽ 3.2 ίϛϡχςΟݕग़ͷ݁Ռ ίϛϡχςΟݕग़ͷ݁Ռʹ͍ͭͯɼ·ͣશମతͳ ʹ͍ͭͯड़ΔɽGirvan–Newman ๏ʹΑΔ ίϛϡχςΟݕग़ʹΑͬͯɼϝΠϯίϯϙʔωϯ
ද2: ڞஶNWͷϝΠϯίϯϙʔωϯτͷجૅత ͳNWಛྔɽ େ͖ͳΫϥελϦϯάͱখ͞ͳฏۉϊʔυؒ ڑΛ࣋ͪɼεϞʔϧϫʔϧυੑΛࣔ͢ɽ ϊʔυ(N) 7 386 Τοδ 29 689 ฏۉϊʔυؒڑ 5.74 ln (N) 8.91 NWີ < 0.01 ΫϥελϦϯά 0.71 τશମ15ݸͷίϛϡχςΟʹׂ͞Εͨɽ֤ί ϛϡχςΟͷαΠζΛද3ʹࣔ͢ɽϊʔυɼΤο δͱʹGN0 ͕શମͷ87%ΛΊ͓ͯΓɼ ͜Ε͋·Γʮ͖Ε͍ͳʯׂͱݴ͑ͳ͍ɽNW ͷߏͷΈ͔ΒίϛϡχςΟ͕໌֬ʹݕग़͞Ε ͣɼະԽͷ෦͕ଟ͘Δ͜ͱʹͳͬͨɽͦΕͰ ɼίϛϡχςΟͷαΠζେ͖͘ͳ͍ͱ͍ ͑ɼ͍͔ͭ͘ͷίϛϡχςΟ͕ݕग़͞Ε͍ͯΔɽҎ ԼͰ֤ίϛϡχςΟͷৄࡉʹ͍ͭͯड़Δɽ ·֤ͣίϛϡχςΟͷಛΛݟΔͨΊʹɼ֤ί ϛϡχςΟͷจࢽγΣΞϕΫτϧΛఆٛ͢Δɽ Girvan–Newman ๏ʹΑΔίϛϡχςΟݕग़ʹΑ ͬͯɼ֤Τοδ֤ίϛϡχςΟɼ͓Αͼίϛϡχ ςΟؒͷΤοδʹׂ͞ΕΔ[6]ɽίϛϡχςΟ xʹ͍ͭͯͷจࢽγΣΞϕΫτϧCx ɼίϛϡ χςΟxʹॴଐ͢ΔΤοδͷจࢽγΣΞϕΫτϧ ͷฏۉͱͯ͠ఆٛ͞ΕΔɽ͢ͳΘͪɼCx ίϛϡ χςΟxͷڞஶ͕ؔͲͷจࢽͷจʹΑͬ ͯߏங͞Εͨͷ͔Λࣔ͢ϕΫτϧͰ͋Δɽ ·ͨɼ֤จࢽͷಛΛݟΔͨΊʹɼ֤จࢽͷ ίϛϡχςΟγΣΞϕΫτϧΛఆٛ͢Δɽ۩ମతʹ ɼจࢽiͷίϛϡχςΟγΣΞϕΫτϧJiͷ ୈxɼ ίϛϡχςΟxʹଐ͢ΔΤοδͷ จࢽγΣΞϕΫτϧͷୈiͷ૯ ϝΠϯίϯϙʔωϯτશମʹଐ͢ΔΤοδͷ จࢽγΣΞϕΫτϧͷୈiͷ૯ Ͱࢉग़͞ΕΔɽ͢ͳΘͪ Ji ɼจࢽ iͷจ ͕ͲͷίϛϡχςΟʹ͞Ε͍ͯΔ͔Λࣔ͢ϕ ΫτϧͰ͋ΔɽຊߘͰɼίϛϡχςΟʹଐͯ͠ ͍ͳ͍Τοδʹ͍ͭͯҰͭʹ·ͱΊɼʮίϛϡχ ςΟؒΤοδʯʹଐ͢Δͷͱͯ͠ѻ͏ɽͦͷͨ ΊɼίϛϡχςΟγΣΞϕΫτϧJi(ίϛϡχ ςΟ+1)࣍ݩͷϕΫτϧͱͳΓɼ֤ͷ૯ ৗʹ1ʹͳΔɽ ද4ʹɼ֤จࢽͷίϛϡχςΟγΣΞϕΫτϧ Λࣔ͢ɽશΤοδͷ87%͕GN0ʹॴଐ͍ͯ͠ Δ͜ͱ͔Βࣗ໌ͳΑ͏ʹɼଟ͘ͷจࢽͷΤοδ ͕GN0ʹूத͍ͯ͠Δɽ͔͠͠ɼதʹGN0 ؼଐ͢Δׂ߹͕50%ΛԼճ͍ͬͯΔจࢽଘࡏ ͓ͯ͠Γɼ۩ମతʹɼຊইɾΦετϛʔɾࣦ ېཧֶձࢽɼຊखज़ޢֶձࢽɼຊ์ࣹઢ ޢֶձࢽͷ3ࢽ͕֘͢Δɽ͜ͷ͏ͪɼຊইɾ Φετϛʔɾࣦېཧֶձࢽͱຊखज़ޢֶձࢽ ɼͦΕͧΕGN1ʹ68%ͱ55%ؼଐ͍ͯ͠Δɽ ίϛϡχςΟͷจࢽγΣΞϕΫτϧͰݟͯΈΔ ͱɼਤ3(a)ʹࣔ͢Α͏ʹɼຊইɾΦετϛʔɾ ࣦېཧֶձࢽͱຊखज़ޢֶձࢽɼGN1ʹ ͓͍ͯେଟͱͳ͍ͬͯΔɽ͜ͷ͜ͱ͔Βɼ͔ͳ Γಠཱੑ͕ߴ͍ݚڀऀίϛϡχςΟΛܗ͍ͯ͠ Δͱݴ͑Δɽຊ์ࣹઢޢֶձࢽʹ͍ͭͯɼ֤ จࢽͷίϛϡχςΟγΣΞϕΫτϧͰGN3ʹ 93%ͱେଟ͕ؼଐ͍ͯ͠Δɽ·ͨਤ3(b)ʹࣔ͢ ίϛϡχςΟͷจࢽγΣΞϕΫτϧͰɼ࠷ଟ Ͱͳ͍ͷͷGN3 ͷ1/3 ऑΛΊΔओཁͳ ߏཁҼͱͳ͍ͬͯΔɽຊޢݚڀֶձࡶࢽ(͓ ΑͼຊޢՊֶձࢽ)نͷେ͖ͳ૯߹ֶձ͕ ൃߦ͢ΔจࢽͰ͋Γɼ֤ઐྖҬʹຬวͳ͘ݱΕ Δ͕͋Δ͜ͱΛߟ͑ΔͱɼΓಠཱͨ͠ݚڀ ऀίϛϡχςΟ͕ܗ͞Ε͍ͯΔͱߟ͑ͯྑͦ͞͏ Ͱ͋Δɽ࣮ࡍʹɼ͜ΕΒͷֶձʹ͍ͣΕઐྖ ҬΛݗҾ͢ΔϦʔμʔతݚڀऀ͕ଘࡏ͠ɼ૯߹ֶձ ͔ΒԽͯ͠ɼϦʔμʔΛத৺ͱֶͨ͠Λܗ͠ ͍ͯΔ͜ͱ͕ΒΕ͍ͯΔɽ͕ͨͬͯ͠ɼݚڀऀί ϛϡχςΟʹ͓͚Δಠཱੑͷߴֶ͍͕ɼڞஶऀ NWͷߏͷΈ͔Βݕग़Ͱ͖Δ߹͕͋Δ͜ͱ͕ ࣔ͞Εͨɽ ٯʹɼද4ʹ֤ࣔ͢จࢽͷίϛϡχςΟγΣΞ ϕΫτϧʹ͓͍ͯશͯͷΤοδ͕GN0ʹؼଐ͢Δ จࢽɼ࿏Ճޢֶձࢽɼຊٹٸޢֶձࡶ ࢽɼߴঁࢠେֶޢֶձࢽɼޢڭҭֶݚڀɼ ຊޢྺֶ࢙ձࢽɼຊੜ৩ޢֶձࢽɼຊϧʔ ϥϧφʔγϯάֶձࢽɼຊޢֶձࢽͷ8ࢽ Ͱ͋Δɽ͜ΕΒཧతͳہॴੑ(࿏Ճޢֶձ
ίϛϡχςΟ ϊʔυ ରMCൺ Τοδ ରMCൺ GN0 6 422 86.95% 25 877 87.16% GN1 293 3.97% 1559 5.25% GN2 143 1.94% 362 1.22% GN3 111 1.50% 442 1.49% GN4 108 1.46% 284 0.96% GN5 77 1.04% 259 0.87% GN6 61 0.83% 122 0.41% GN7 40 0.54% 60 0.20% GN8 23 0.31% 154 0.52% GN9 21 0.28% 64 0.22% GN10 19 0.26% 46 0.15% GN11 18 0.24% 25 0.08% GN12 18 0.24% 52 0.18% GN13 17 0.23% 25 0.08% GN14 15 0.20% 44 0.15% Main Component 7 386 – 29 689 – Entire NW 9 478 – 32 858 – ࢽɼߴঁࢠେֶޢֶձࢽɼຊޢֶձࢽ)ɼ ͋Δ͍ઐతͳہॴੑ(ຊٹٸޢֶձࡶ ࢽɼޢڭҭֶݚڀɼຊޢྺֶ࢙ձࢽɼຊੜ ৩ޢֶձࢽɼຊϧʔϥϧφʔγϯάֶձࢽ)͕ ڧ͍ͨΊʹɼձһ͕গͳ͘ͳΓɼͦͷͨΊʹGN0 ͔ΒԽͰ͖Δ΄Ͳͷنͷֶͱͳ͍ͬͯͳ͍ɼ ͱ͍͏ݪҼ͕ߟ͑ΒΕΔɽ
4
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ຊߘͰɼޢܥݚڀͷจݙใΛσʔλϕʔε ͔Βநग़ɼڞஶNWΛߏங͠ɼͦͷಛʹ͍ͭͯ໌ Β͔ʹͨ͠ɽڞஶNWͷϝΠϯίϯϙʔωϯτɼ εέʔϧϑϦʔੑʹ͍ͭͯऑ͍ҰํͰɼ໌֬ͳε ϞʔϧϫʔϧυੑΛ༗͢Δ͜ͱ͕֬ೝ͞Εͨɽ·ͨ ϝΠϯίϯϙʔωϯτʹରͯ͠Girvan–Newman ๏ʹΑΔίϛϡχςΟݕग़Λߦ͍ɼͦͷ݁Ռ1ͭͷ ڊେίϛϡχςΟͱͦͷଞͷখ͞ͳ14ݸͷίϛϡ χςΟʹׂ͞Εͨɽݕग़͞ΕͨίϛϡχςΟͷத ʹɼઐྖҬΛݗҾ͢ΔϦʔμʔʹΑͬͯܗ͞ Εͨಠཱੑͷߴֶ͍ʹରԠ͢Δͷ͋Γɼݚڀ ऀίϛϡχςΟʹ͓͚Δֶ͕ɼڞஶऀNWͷߏ ͷΈ͔Βݕग़Ͱ͖Δ߹͕͋Δ͜ͱ͕ࣔ͞Εͨɽ ࠓޙͷ՝ͱͯ͠ɼ·ͣίϛϡχςΟݕग़ͷਫ਼៛ Խ͕ڍ͛ΒΕΔɽຊߘͰϕʔγοΫͳख๏Ͱ͋Δ Girvan–Newman๏Λ༻͍͕ͨɼ΄ͱΜͲ͕1ͭ ͷେ͖ͳίϛϡχςΟʹׂΓͯΒΕɼ͋·Γ͖Ε ͍ͳׂͱͳΒͳ͔ͬͨɽ͜ΕNW͕࣋ͭຊ ࣭తͳಛ͕ݪҼͰ͋Δ͜ͱߟ͑ΒΕΔ͕ɼί ϛϡχςΟݕग़ख๏ͷͰ͋ΔՄೳੑ͋Δɽί ϛϡχςΟݕग़ख๏ۙٸͳൃలΛݟ͓ͤͯ Γɼݕग़ΛߴԽ͢ΔΞϧΰϦζϜͷ։ൃਐΜͰ ͍Δɽ͜ΕΒͷίϛϡχςΟݕग़ख๏Λ༻͍Δ͜ͱ ʹΑͬͯɼྑͳίϛϡχςΟ͕ಘΒΕΕɼͦΕ ʹΑͬͯޢܥݚڀΛ၆ᛌతʹݟΔ͜ͱɼޢܥ ݚڀʹ͓͚ΔઐྖҬؒͷؔੑʹ͍ͭͯࣔࠦΛಘ Δ͜ͱ͕ظͰ͖ΔͩΖ͏ɽ΄͔ʹɼଞͷֶ ͷڞஶNWͱͷൺֱ͕ڍ͛ΒΕΔɽ͜ΕʹΑΓɼ ଟ͘ͷڞஶNW͕ීวతʹ࣋ͭಛͱޢܥݚڀ(a) GN1 (b) GN3 ਤ3: GN1ͱGN3ͷจࢽγΣΞϕΫτϧ ͷNW͚͕ͩ࣋ͭ૬ରతಛΛ໌֬ʹ۠ผ͢Δ͜ ͱ͕Ͱ͖ΔΑ͏ʹͳΔɽ·ͨɼൃలΛֶ͛ͨ ʹ͓͚ΔڞஶNWͷܦ࣌తมԽΛௐΔ͜ͱͰɼ ݱࡏͷޢܥݚڀ͕ظɾԁख़ظɾਰୀظͷͲͷ εςʔδʹ͋Δͷ͔Λ໌Β͔ʹ͢Δ͜ͱ͕ظͰ͖ Δ͠ɼޢܥݚڀ͕ࠓޙൃల͍ͯͨ͘͠ΊʹͲͷΑ ͏ʹNWߏΛมԽ͍͚ͤͯ͞ྑ͍͔Λ໌Β͔ ʹͰ͖ΕɼޢܥݚڀऀؒͷίϥϘϨʔγϣϯΛ ଅਐ͢ΔͨΊͷํࡦ͕ݟ͍ͩͤΔ͔͠Εͳ͍ɽͦ ͷଞͷ՝ͱͯ͠ɼNWಛྔʹΑΔ֤ݚڀऀͷ ׆ಈͷಛ͚ͮɼ໊دͤͷํ๏ͷݕ౼ͳͲ͕ڍ͛Β ΕΔɽ
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ຊݚڀJSPSՊݚඅ16H02693ͷॿΛड͚ ͨͷͰ͋Δɽ ʲʳ [1]http://www.jamas.or.jp/user/database/ index.html [2] ޢܥͷจࢽʹ͓͍ͯɼʮݪஶจʯͷ ΄͔ɼʮஃʯɼʮݚڀใࠂʯɼʮ࣮ફใࠂʯɼʮ૯ આʯɼʮࢿྉʯͳͲͷ͕۠͋Δɽ [3] ݸʑͷࣄྫʹରԠ͢Δ͜ͱՄೳͰ͋Δ͕ɼ ͦͷ࡞ۀྔେͱͳΔɽ·ͨɼ໊دͤͷର ԠΛͲ͜·ͰࢪͤଥͰ͋Δ͔ʹ͍ͭͯ ͷҰൠతͳج४ͳ͍ͨΊʹɼ໊دͤରԠϨ ϕϧͷબ͕ዞҙతͱͳΒ͟ΔΛಘͳ͍͜ ͱɼݟૹͬͨཧ༝Ͱ͋ΔɽՊݚඅਃʹ ༻͍ΒΕΔলڞ௨ݚڀ։ൃཧγεςϜ (e-Rad)Ͱɼ֤ݚڀऀʹݚڀऀ൪߸Λ༩ ͢Δ͜ͱͰ໊ٛͷҰݩԽΛ࣮ݱ͍ͯ͠Δ͕ɼ ͜͏͍ͬͨํ๏Λ࠾Δ͜ͱͷͰ͖Δ໘ݶ ΒΕΔɽ [4] શମΛׂͨ͠෦ʹ͍ͭͯɼ௨ৗʮΫϥ ελʯͱݺΕΔ͜ͱ͕ଟ͍͕ɼNWՊֶͷ ʹɼΫϥελϦϯάͱ͍͏ॏཁͳ ಛྔ͕͋ΓɼͦΕͱͷࠞಉΛආ͚ΔͨΊʹ ʮίϛϡχςΟʯͱ͍͏༻ޠΛ͏͜ͱ͕ଟ ͍Α͏Ͱ͋Δɽ [5] σʔλΛΫϥελϦϯά͢Δख๏ͷ͏ͪɼ σʔλ͕͋Δ1 ͭͷΫϥελʹॴଐ͢Δख ๏ΛϋʔυΫϥελϦϯάɼσʔλ͕ෳͷ Ϋϥελʹଐ͢Δ͜ͱΛڐ༰͢Δख๏Λιϑ τΫϥελϦϯάͱݺͿɽGirvan–Newman ๏ʹؔͯ͠ݴ͑ɼϊʔυ͕1ͭͷίϛϡχ ςΟʹଐ͢Δख๏Ͱ͋Δɽ [6] ݫີʹɼGirvan–Newman๏ʹΑͬͯί ϛϡχςΟʹׂ͞ΕΔͷϊʔυͰ͋Δɽ Τοδͷॴଐɼ֤ίϛϡχςΟʹॴଐ͢Δ ϊʔυʹΑͬͯ༠ಋ͞ΕΔ༠ಋ෦άϥϑʹ Αͬͯఆ·Δɽ͕ͨͬͯ͠ɼશͯͷΤοδ͕ ίϛϡχςΟʹॴଐ͢ΔͱݶΒͣɼҟͳΔ ίϛϡχςΟʹଐ͢Δϊʔυಉ࢜Λ݁ͿΤο δɼίϛϡχςΟؒΛ݁ͿΤοδͱͳΔɽ[2] Newman, M. E. J. and Girvan, M., “Finding and evaluating com-munity structure in networks,”Physical Review E, 69(026113) (2004). [3] ࣰా༞,ʮຊʹ͓͚Δਓೳݚڀͷܥ ේʯɼʰਓೳֶձࢽʱ, 26(6), pp. 584–589 (2011). [4] দాޫ৴,ʢฤʣʰ࣮ફೳྗΛຏ͘ޢݚڀ -ਫ਼ਆޢֶରԠʱɼۚ๕ಊ(2009). [5] ਿࢁߒฏɾେ࡚ത೭ɾࠓᚸ,ʮจͷҾ༻ɾ ڞஶ͔ؔΒԿ͕͔Δ͔? : ωοτϫʔΫ ੳख๏͔ΒͷΞϓϩʔνʯɼʰిࢠใ௨৴ ֶձٕज़ݚڀใࠂ. IN,ใωοτϫʔΫʱ, 106(42), pp. 85–90 (2006).
ද 4: ֤จࢽͷίϛϡχςΟγΣΞϕΫτϧ จࢽ GN GN GN GN GN GN GN GN GN GN GN GN GN GN GN ίϛϡχςΟ 01 2345 6 7 8 9 10 11 12 13 14 ؒΤοδ ຊޢՊֶձࢽ 95% 0% 1% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% ࿏Ճޢֶձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊ͕Μޢֶձࢽ 97% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% ຊޢֶڭҭֶձࢽ 91% 0% 2% 4% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% ຊޢཧֶձࢽ 99% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% ຊޢݚڀֶձࡶࢽ 81% 0% 7% 8% 0% 0% 1% 1% 0% 0% 0% 0% 0% 0% 0% 2% ຊٹٸޢֶձࡶࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊΫϦςΟΧϧέΞޢֶձࢽ 95% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊެऺӴੜޢֶձࢽ 88% 0% 0% 0% 0% 11% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% ຊখࣇޢֶձࢽ 98% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% ຊॿ࢈ֶձࢽ 83% 4% 0% 0% 0% 0% 0% 2% 0% 8% 0% 0% 0% 1% 0% 2% ຊਫ਼ਆอ݈ޢֶձࢽ 91% 0% 8% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 1% ຊইɾΦετϛʔɾࣦېཧֶձࢽ 27% 68% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 3% ຊҬޢֶձࢽ 84% 0% 2% 0% 0% 9% 0% 0% 0% 0% 2% 0% 2% 0% 0% 2% ຊපڭҭɾޢֶձࢽ 99% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊੑޢֶձࢽ 95% 0% 0% 0% 0% 0% 1% 4% 0% 0% 0% 0% 0% 1% 0% 0% ຊ॥ثޢֶձࢽ 87% 1% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 8% 1% ߴঁࢠେֶޢֶձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ઍ༿ޢֶձձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ΞσΟΫγϣϯޢ 84% 0% 0% 0% 0% 0% 0% 0% 16% 0% 0% 0% 0% 0% 0% 0% ຊӡಈثޢֶձࢽ 92% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 7% Ոޢֶݚڀ 77% 0% 8% 0% 2% 0% 6% 1% 0% 0% 0% 3% 0% 0% 0% 4% ຊޢҩྍֶձࡶࢽ 99% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊޢٕज़ֶձࢽ 90% 3% 2% 3% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ޢڭҭֶݚڀ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ޢஅ 79% 0% 0% 0% 0% 0% 0% 0% 20% 0% 0% 0% 0% 0% 0% 1% ຊޢࢱֶձࢽ 59% 0% 0% 0% 38% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 2% ຊޢྙཧֶձࢽ 81% 0% 0% 1% 0% 0% 0% 5% 0% 0% 0% 0% 0% 7% 0% 6% ຊޢྺֶ࢙ձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊࡂޢֶձࢽ 99% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊࡏέΞֶձࢽ 92% 0% 1% 0% 3% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% ຊखज़ޢֶձࢽ 38% 55% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% ຊ৽ੜࣇޢֶձࢽ 95% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊਛෆશޢֶձࢽ 96% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% ຊੜ৩ޢֶձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊेࣈޢֶձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊපޢֶձࢽ 99% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊ์ࣹઢޢֶձࢽ 5% 0% 0% 93% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% ຊࢠޢֶձࢽ 89% 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% 0% 5% 0% 0% ຊຫੑޢֶձࢽ 98% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊϧʔϥϧφʔγϯάֶձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ޢֶ 90% 2% 0% 0% 2% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% ຊޢֶձࢽ 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ຊχϡʔϩαΠΤϯεޢֶձࢽ 92% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5%
