\u203bFor English please scroll down.<\/p>\n
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\u3010\u30bf\u30a4\u30c8\u30eb\u3011Analysing network data from unstructured sources<\/p>\n
\u3010\u6982\u8981\u3011<\/p>\n
The network perspective has proven a fruitful conceptual framework and analysis tool for understanding how societies and communities are organised and connected. Social networks afford the notion that behaviours and attitudes and other outcomes of individuals are potentially contingent on their personal contacts as well as their individual properties and properties of the contexts in which they are embedded. The canonical form of network data is collected through a roster method that prompts respondents to report their interactions to everyone in a predefined set of people. This requires a census of the members of, or frame for, the relevant set and in many research settings this is not a priori possible. Various link-tracing designs have been proposed in the literature for eliciting network data (or to simply recruit respondents) in environments where no such frame is available. We propose here to not only allow for tracing links between individuals but also to trace the affiliations to organisations and settings of individuals, as well as using information of how these organisations and settings are connected. The target of inference is to infer the tie-formation processes and to that effect we assume a multilevel exponential random graph model. A Bayesian approach has recently been proposed for accounting for the sampled nature of data and here we extend this procedure to handle the different types of ties of the overall network. We use two illustrative examples. The first example is an analysis of a network collated from newspaper reports on terrorist activities in Indonesia that we treat as a gold standard for appraising the veracity of the inference scheme. The second example is based on a network collected in the wild in the Eastern Democratic Republic of Congo, stemming from a project aimed at investigating the post-conflict livelihoods of combatants, civilians, and demobilised people. The key types of ties are personal and job-seeking ties of individuals, the affiliations with civic institutions and different armed groups, as well as alliance ties between armed groups. The collected data consist of a number of partially overlapping personal networks with individual affiliations. In addition to the substantive interest in modelling the ties between individuals, the inference scheme provides predictions of missing ties that can be used for studying centrality of different actors (people and armed groups) in the population.<\/p>\n
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*English<\/p>\n
This notice is to inform you of the Waseda Institute of Political Economy (WINPEC) Seminar on Oct. 24th. This seminar is co-hosted by Top Global University Project (TGU).<\/p>\n
\u3010TIME\u301124th, October Tuesday, 10:40-12:10<\/p>\n
\u3010Venue\u3011The meeting room (conference room) #1, BUILDING 3 – Floor 10.<\/p>\n
\u3010Presenter\u3011Johan Koskinen\u00a0 (University of Manchester, School of Social Sciences, Social Statistics, Lecturer)<\/p>\n
\u3010Title\u3011Analysing network data from unstructured sources<\/p>\n
\u3010 Abstract \u3011<\/p>\n
The network perspective has proven a fruitful conceptual framework and analysis tool for understanding how societies and communities are organised and connected. Social networks afford the notion that behaviours and attitudes and other outcomes of individuals are potentially contingent on their personal contacts as well as their individual properties and properties of the contexts in which they are embedded. The canonical form of network data is collected through a roster method that prompts respondents to report their interactions to everyone in a predefined set of people. This requires a census of the members of, or frame for, the relevant set and in many research settings this is not a priori possible. Various link-tracing designs have been proposed in the literature for eliciting network data (or to simply recruit respondents) in environments where no such frame is available. We propose here to not only allow for tracing links between individuals but also to trace the affiliations to organisations and settings of individuals, as well as using information of how these organisations and settings are connected. The target of inference is to infer the tie-formation processes and to that effect we assume a multilevel exponential random graph model. A Bayesian approach has recently been proposed for accounting for the sampled nature of data and here we extend this procedure to handle the different types of ties of the overall network. We use two illustrative examples. The first example is an analysis of a network collated from newspaper reports on terrorist activities in Indonesia that we treat as a gold standard for appraising the veracity of the inference scheme. The second example is based on a network collected in the wild in the Eastern Democratic Republic of Congo, stemming from a project aimed at investigating the post-conflict livelihoods of combatants, civilians, and demobilised people. The key types of ties are personal and job-seeking ties of individuals, the affiliations with civic institutions and different armed groups, as well as alliance ties between armed groups. The collected data consist of a number of partially overlapping personal networks with individual affiliations. In addition to the substantive interest in modelling the ties between individuals, the inference scheme provides predictions of missing ties that can be used for studying centrality of different actors (people and armed groups) in the population.<\/p>\n
<\/p>\n
\u3010Organizer\u3011Prof. Yasuyuki Todo<\/p>\n
\u3010Language\u3011 English<\/p>\n
We encourage your participation.<\/p>\n
<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
\u203bFor English please scroll down. \uff11\uff10\u6708\uff12\uff14\u65e5\uff08\u706b\uff09\u306b\u884c\u308f\u308c\u307e\u3059\u3001\u73fe\u653f\u7814\u30bb\u30df\u30ca\u30fc\u306e\u65e5\u7a0b\u3001\u5834\u6240\u306a\u3069\u306b\u3064\u304d\u307e\u3057\u3066\u3054\u6848\u5185\u7533\u3057\u4e0a\u3052\u307e\u3059\u3002\u306a\u304a\u3001\u672c\u30bb\u30df\u30ca\u30fc\u306fSGU\uff08\u30b9\u30fc\u30d1\u30fc\u30b0\u30ed\u30fc\u30d0\u30eb\u5927 […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[103,104,105,1],"tags":[99,18],"class_list":["post-5757","post","type-post","status-publish","format-standard","hentry","category-news-en","category-other-en","category-other","category-news","tag-events-en","tag-events"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/posts\/5757","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/comments?post=5757"}],"version-history":[{"count":0,"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/posts\/5757\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/media?parent=5757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/categories?post=5757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.waseda.jp\/fpse\/winpec\/wp-json\/wp\/v2\/tags?post=5757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}