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Outline

Title
Abstract
Figures
Introduction
Social Network Analysis: Basics
Social Networks: Analysis Metrics and Performance
Social Networks Analysis Metrics
Missing Data in a Social Network
Finding the Shortest Path
Conclusions
References

Social Networks: Computational Aspects and Mining

Profile image of Mrutyunjaya PandaMrutyunjaya Panda
https://doi.org/10.1007/978-1-4471-4054-2
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21 pages

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Abstract

Social Network Analysis (SNA) is becoming an important tool for investigators, but all the necessary information is often available in a distributed environment. Currently there is no information system that helps managers and team leaders monitor the status of a social network. This chapter presents an overview of the basic concepts of social networks in data analysis including social network analysis metrics and performances. Different problems in social networks are discussed such as uncertainty, missing data and finding the shortest path in a social network. Community structure, detection and visualization in social network analysis is also illustrated. This chapter bridges the gap among the users by combining social network analysis methods and information visualization technology to help a user visually identify the occurrence of a possible relationship amongst the members in a social network. The chapter illustrates an online visualization method for a DBLP N. Ghali ( )

Figures (4)
a long period of time, the actuality and clarity of the author’s collaboration network is often lost. A key requirement was the need for the visualization of an author and his/her academic network in the context of their publication activities. The online tool is built over the DBLP dataset and the dataset contains information about 913,534 authors from the field of computer science and 5,192,020 interactions between these authors [17].
a long period of time, the actuality and clarity of the author’s collaboration network is often lost. A key requirement was the need for the visualization of an author and his/her academic network in the context of their publication activities. The online tool is built over the DBLP dataset and the dataset contains information about 913,534 authors from the field of computer science and 5,192,020 interactions between these authors [17].
only dependent or independent co-authors). The middle part contains three groups of panels. The first group contain values of author measures (such as stability, cooperativeness, topics, etc.) over the time period. If you select an edge with a co-author, the second group of panels will appear. These panels contains values of relation measures (stability of the relation, dependency between authors and the topics of the relation). By clicking somewhere in the timeline you can switch the view to a different point in time. The third group of panels contains global values of the whole dataset (such as number of publications, distribution of particular measures over the authors). Figure 1.2 depicts Ajith Abraham’s network with a different scale.
only dependent or independent co-authors). The middle part contains three groups of panels. The first group contain values of author measures (such as stability, cooperativeness, topics, etc.) over the time period. If you select an edge with a co-author, the second group of panels will appear. These panels contains values of relation measures (stability of the relation, dependency between authors and the topics of the relation). By clicking somewhere in the timeline you can switch the view to a different point in time. The third group of panels contains global values of the whole dataset (such as number of publications, distribution of particular measures over the authors). Figure 1.2 depicts Ajith Abraham’s network with a different scale.
matrix was compared with the original one [45]. A change from zero to one in the reduced matrix can be in a wider sense considered as a link suggestion. In different fields, the suggestion can have different meanings. In the terrorist network, we can consider them as suggestions for investigation to determine whether the link truly exists in reality; for more detail the reader may refer to [45]. The results for a rank parameter setting equal to 10 are illustrated in Fig. 1.3. Same colouring is used as in the original paper [25] by Krebs. Green triangles represent flight AA #11, which crashed into WTC North, full red squares represent flight AA #77 which crashed into the Pentagon, empty blue squares represent flight UA #93 which crashed in Pennsylvania, and full purple diamonds represent flight UA #175 which crashed into WTC South.
matrix was compared with the original one [45]. A change from zero to one in the reduced matrix can be in a wider sense considered as a link suggestion. In different fields, the suggestion can have different meanings. In the terrorist network, we can consider them as suggestions for investigation to determine whether the link truly exists in reality; for more detail the reader may refer to [45]. The results for a rank parameter setting equal to 10 are illustrated in Fig. 1.3. Same colouring is used as in the original paper [25] by Krebs. Green triangles represent flight AA #11, which crashed into WTC North, full red squares represent flight AA #77 which crashed into the Pentagon, empty blue squares represent flight UA #93 which crashed in Pennsylvania, and full purple diamonds represent flight UA #175 which crashed into WTC South.

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