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In the first case, the network, or by implication the underlying social structure, would be called assortative in the second case it is disassortative. We can look at whether important people preferentially exchange emails with each other or whether they instead communicate with less well connected people. People that receive many emails may be important, and those that send many emails might be influential. We can then analyze the network and draw conclusions about the underlying social structure. We can also count how many emails have passed in either direction assigning that number to the corresponding edge would produce a weighted network. For instance, we can distinguish whether there has been a message from A to B, one from B to A, or both. We can just leave it at that and formally analyze the resulting network, or we can decide to represent more details. There is a relation-formally, a link or an edge in the network-between A and B if an email message between them has been recorded. These people constitute the elements or agents-or, in the formal terms of network analysis, the vertices or nodes-of the network.
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absent) they may also be directed (from A to B, but not necessarily also from B to A) and they may also be quantitative-that is, they may possess strengths or weights.Īn easy example: data about the email exchanges within a certain group of people. The relations may simply be qualitative (present vs. The basic idea is that the system to be analyzed consists of elements that stand in pairwise relations. That is, we need to agree on some class of structures and then see what specific features and properties the particular structure possesses.Ī general such class is that of networks. To achieve this, we need to start with some structural hypotheses.
NETWORK ANALYSIS DEFINITION HOW TO
The questions then are what structure to extract, what structure to expect to emerge from the data, and how to make that structure interpretable for humans. It can serve as a basis to extract structure by computational methods. Making things digital means representing them as bit strings.
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The aim is to provide an overview of network techniques for historians looking to add robust network analysis to their research repertoire. The essay illustrates how interpretable historical conclusions are drawn from a variety of quantitative metrics. Four distinct networks, including those focused on authors, keywords, and citations, quickly unearth a range of relevant historical information. To demonstrate the power of the approach, the essay applies network theory to a corpus of publications in evolutionary medicine. As a complement to close analysis of particular documents, network analysis can give a large-scale perspective on the history of science, identifying relational patterns across a vast number of documents that might otherwise require an entire career to digest. This essay summarizes an approach to this problem that uses computational techniques of network analysis. Even focusing on a particular scientific field, the rate of new publications far outpaces even the most studious historian’s research capacity. Introduction Since reasoning in multi-dimensional domains tends to be infeasible in the domains as a whole-and the more so, if uncertainty and/or imprecision are involved-decomposition techniques, that reduce the reasoning process to computations in lo.Traditional historical scholarship struggles to keep up with the rapid pace of modern scientific publication trends. Keywords: Decomposition, uncertain reasoning, probabilistic networks, possibilistic networks, learning from data. how to determine from a database of sample cases an appropriate decomposition of the underlying probability or possibility distribution. we also discuss how to learn probabilistic and possibilistic networks from a data, i.e. Since constructing probabilistic and possibilistic networks by hand can be tedious and time-consuming. : In this paper we explain in a tutorial manner the technique of reasoning in probabilistic and possibilistic network structures, which is based on the idea to decompose a multi-dimensional probability or possibility distribution and to draw inferences using only the parts of the decomposition.