Otte, E & Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28 (6) 2002, pp. 441–453.
network analysis
在資訊計量學的範疇內，合作、引用、共被引等許多關係都可以被考慮用來建置社會網絡(social networks)。本研究簡介社會網絡分析(social network analysis)的概念和發展與在資訊科學裡的相關研究和文獻，並利用資訊計量學和網絡分析對於社會網絡分析進行探討。本研究強調在社會脈絡下，比起行動者(actor)本身的特質，社會網絡分析更著重於行動者之間的關係，有必要兼顧兩者來完全了解社會現象。此外，社會網絡分析的研究也嘗試了解結構性的規律(structural regularities)如何影響行動者的行為。在社會網絡分析的概念上，首先區分以特定行動者之關係為中心的自我網絡(ego network)與全部參與者之關係的整體網絡(global network)。網絡以圖形(graph)來表示，每個行動者對應到圖形上的一個節點(nodes)，行動者之間如果具有分析的關係，圖形上的節點之間便有一條連結線(link)；如果行動者間的關係不對稱，則建構起來的圖形是有方向性的(directed)；否則，此圖形為無方向性的(undirected)。兩個節點之間的路徑(path)在社會網絡分析的定義是由連結行動者間的不同連結線所構成，連結線的數目為該路徑的長度。圖形上彼此間相連結的節點與其之間的連結線組成成分(component)，成分上的每一對節點彼此間至少存在一條路徑，並且兩個節點之間如果有路徑相通，則這兩個節點同在一個成分上。完全圖形(complete graph)上的每一對節點之間都有連結線相連。圖形的密度(density)是圖形上的所有連結線數目除以相同節點數目的完全圖形的連結線數。節點的程度中心性(degree centrality)為連結這個節點的連結線數目除以節點數目減一，接近中心性(closeness centrality)為節點數目減一除以這個節點到其他節點的最短路徑長度的總和，中介中心性(betweenness centrality)是除此節點外的任兩個節點之最短路徑會通過此節點之平均比率除以圖形上除此節點外任何兩節點的數目((n-1)(n-2)/2=(n2-3n+2)/2)。派系(clique)則是圖形上一群彼此間有連結線相連的節點與其連結線構成的次圖形(subgraph)。社會網絡分析的發展為1980年代開始，其興起的原因包括Barry Wellman所成立的研究專業組織the International Network for Social Network Analysis(INSNA)以及專書與分析軟體的大量出現，著名的專書有Knoke & Kuklinski(1982)、Wellman & Berkowitz(1988)、Scott(1991)和Wasserman & Faust(1994)等，軟體則有UCInet、Gradap、Multinet、Negopy以及Pajek等等。1601筆社會網絡分析相關論文裡出現3次以上的作者共有133位，他們所形成的合著網絡上最大的成分共有57位作者，密度只有0.05，很明顯地是一個相當稀疏的網絡，程度中心性最高的作者是Barry Wellman，共曾經和其他9位作者合作，接近中心性與中介中心性最高的作者都是Patrick Doreian，表示他連結到所有的作者的路徑最短並且連結許多不同群的作者。
In informetrics, researchers study citation networks, co-citation networks, collaboration structures and other forms of social interaction networks [11–19].
This individualistic approach ignores the social context of the actor [21]. One could say that properties of actors are the prime concern here.
In SNA, however, the relationships between actors become the first priority, and individual properties are only secondary. Relational data are the focus of the investigations.
It should be pointed out, however, that individual characteristics as well as relational links are necessary in order to fully understand social phenomena [21].
In SNA, however, the relationships between actors become the first priority, and individual properties are only secondary. Relational data are the focus of the investigations.
It should be pointed out, however, that individual characteristics as well as relational links are necessary in order to fully understand social phenomena [21].
Wetherell et al. [22, p. 645] describe SNA as follows:
Most broadly, social network analysis (1) conceptualises social structure as a network with ties connecting members and channelling resources, (2) focuses on the characteristics of ties rather than on the characteristics of the individual members, and (3) views communities as ‘personal communities’, that is, as networks of individual relations that people foster, maintain, and use in the course of their daily lives.
Most broadly, social network analysis (1) conceptualises social structure as a network with ties connecting members and channelling resources, (2) focuses on the characteristics of ties rather than on the characteristics of the individual members, and (3) views communities as ‘personal communities’, that is, as networks of individual relations that people foster, maintain, and use in the course of their daily lives.
Another important aspect of SNA is the study of how structural regularities influence actors’ behaviour.
In ‘ego’ studies the network of one person is analysed. ...
In global network analyses one tries to find all relations between the participants in the network.
In global network analyses one tries to find all relations between the participants in the network.
A directed graph G, a digraph, consists of a set of nodes, denoted as N(G), and a set of links (also called arcs or edges), denoted as L(G). ... In sociological research nodes are often referred to as ‘actors’. A link e, is an ordered pair (i,j) representing a connection from node i to node j. Node i is called the initial node of link e, i = init(e), and node j is called the final node of the link: j = fin(e).
If the direction of a link is not important, or equivalently, if existence of a link between nodes i and j necessarily implies the existence of a link from j to i, we say that this network is an undirected graph.
A path from node i to node j is a sequence of distinct links (i, u1), (u1,u2), . . ., (uk,j). The length of this path is the number of links (here k+1).
An undirected graph can be represented by a symmetrical matrix M = (mij), where mij is equal to 1 if there is an edge between nodes i and j, and mij is 0 if there is no direct link between nodes i and j.
A component of a graph is a subset with the characteristic that there is a path between any node and any other one of this subset. If the whole graph forms one component it is said to be totally connected.
The density is an indicator for the general level of connectedness of the graph. If every node is directly connected to every other node, we have a complete graph. The density of a graph is defined as the number of links divided by the number of vertices links in a complete graph with the same number of nodes.
Degree centrality of a node is defined as the number of ties this node has (in graph-theoretical terminology, the number of edges adjacent to this node). ... The degree centrality in an N-node network can be standardized by dividing by N–1: dS(i) = d(i)/(N-1).
Closeness centrality of a node is equal to the total distance (in the graph) of this node from all other nodes. ... Closeness is an inverse measure of centrality in that a larger value indicates a less central actor while a smaller value indicates a more central actor. For this reason the standardized closeness is defined as cS(i) = (N–1)/c(i), making it again a direct measure of centrality.
Finally, betweenness centrality may be defined loosely as the number of times a node needs a given node to reach another node. Stated otherwise, it is the number of shortest paths that pass through a given node. ... Betweenness gauges the extent to which a node facilitates the flow in the network. It can be shown that for an N-node network the maximum value for b(i) is (N2-3N+2)/2. Hence the standardized betweenness centrality is: bs(i) = 2b(i)/(N2-3N+2).
A clique in a graph is a subgraph in which any node is directly connected to any other node of the subgraph.
The three graphs (Figs 2–4) demonstrate the fact that it was only in the early 1980s that SNA started its career. The main reasons for this are the institutionalization of social network analysis since the late 1970s, and the availability of basic textbooks and computer software.
The institutionalization of the field began with the foundation in 1978 by Barry Wellman of the International Network for Social Network Analysis
(INSNA). This is the professional association for researchers interested in social network analysis. Its principal functions are the publication of the informal bulletin Connections, containing news, scholarly articles, technical columns, abstracts and book reviews; sponsoring the annual International Social Networks Conference (also known as Sunbelt) and maintaining electronic, web-based services for its members. The society also publishes, in association with Elsevier, the peer-reviewed international quarterly Social Networks.
(INSNA). This is the professional association for researchers interested in social network analysis. Its principal functions are the publication of the informal bulletin Connections, containing news, scholarly articles, technical columns, abstracts and book reviews; sponsoring the annual International Social Networks Conference (also known as Sunbelt) and maintaining electronic, web-based services for its members. The society also publishes, in association with Elsevier, the peer-reviewed international quarterly Social Networks.
The earliest basic text that the authors know of dealing exclusively with social network analysis is Knoke and Kuklinski’s Network Analysis, published in1982. Other important books having influenced the growth of the discipline are Wellman and Berkowitz’ Social Structures: a Network Approach (1988), Scott’s Social Network Analysis: a Handbook (1991), and Wasserman and Faust’s Social Network Analysis: Methods and Applications (1994).
The development of dedicated software also led to an increase in interest in the field and its methods. The best-known (and very user-friendly) program for the analysis of social networks is UCInet. ... UCInet can easily be combined with Krackplot, a well-known program for drawing social maps. Other examples of computer programs for social network analysis are Gradap, Multinet, Negopy and Pajek.
In the 1601 articles dealing with SNA there were 133 authors occurring three times or more. Forming an undirected co-authorship graph (of these 133 authors) led to a big connected component of 57 authors, two components of four authors, two components of three authors, seven small components consisting of two authors and 48 singletons.
The density for the central network of network analysts is 0.05, so this network is clearly not dense at all, but very loose.
In this network being a central author means that this scientist has collaborated (in the sense of co-authored) with many colleagues. The author with the highest degree centrality is Barry Wellman (University of Toronto), who has a degree centrality of 9.
A high closeness for an actor means that he or she is related to all others through a small number of paths. The most central author in this sense is Patrick Doreian (University of Pittsburgh).
Actors with a high betweenness play the role of connecting different groups, as ‘middlemen’. Again Patrick Doreian has the highest betweenness.
A small-world network is then characterized as a network exhibiting a high degree of clustering and having at the same time a small average distance between nodes. Moreover, the ‘hubs’ and ‘authorities’ approach is related to the Pinski–Narin influence weight citation measure [46] and mimics the idea of ‘highly cited documents’ (authorities) and reviews (hubs) [1].
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