TABLE B.1 Feature Comparison

Feature

Social Network

Link Analysis

Dynamic Network Analysis

Entity studied

The network

A set of links

Either the network or a set of links

Multilink

One or two links

Many links

One or many links

Multimode

One or two modes

Many modes

One or many modes

Focus

Identify key actors and groups

Anomaly detection

Identify key actors and groups

Networks evolve?

No

No

Yes

Locates network elite?

Yes

No

Yes

Locates patterns of behavior?

No

Yes

No

Locates patterns across networks?

No

Needs work

Needs work

What evolves?

Nothing

Nothing

Agents, groups, and networks

Predicts and assesses individual behavior

Few behaviors

Many behaviors

Many behaviors

Predicts and assesses group behavior

Few behaviors

No

Few behaviors

Handles missing information?

No

Needs work

Needs work

Optimized search?

No

Yes

Sometimes

Locates groups?

Yes

Yes

Yes

Analysis of change

Qualitative

Assumes the future is the same as the past

Quantitative

Handles streaming data

No

Needs work

Needs work

on characterizing the size and shape (topology) of the underlying networks, identifying who stands out (which individuals because of their relations to others occupy key positions in the network), and how the structure of the network or an individual’s position within it influences behavior. There are numerous SNA computational tools, ranging from network visualizers to packages for analyzing network data, and new ones appear daily.

Link analysis centers on discovering patterns by looking at the relations among entities. Analysts in this area use computational techniques to locate patterns and subgroups. This area has emerged largely from computer science, with particular attention to work in machine learning. Some of the roots in this area are in forensics. Extraction of links often requires massive data preprocessing or restructuring of databases (Goldberg and Wong, 1998). Advanced data-processing techniques are combined with machine learning to enable rapid database transformation and pattern extraction. Much of the work in this area has focused on the identification and recognition of patterns, data mining, and node iden tification. There are a growing number of tools, many of which are available on the Web. Common tools exist for doing a variety of tasks, including extracting links from databases (Goldberg and Senator, 1998) and texts (Lee, 1998) and analyzing the extracted links (Chen and Lynch, 1992; Hauck et al., 2002).

Dynamic network analysis (DNA) is an emergent field centered on the collection, analysis, understanding, and prediction of dynamic relations (such as who talks to whom) and the impact of such dynamics on the behavior of individuals or collectives (Carley, 2003). Analysts combine computational techniques, such as machine learning and artificial intelligence, with traditional graph and social network theory and with empirical research on human behavior, groups, organizations, and societies to develop and test tools and theories of relational enabled and constrained action. This area builds on social network analysis and link analysis and adds computer simulation to the mix to look at network evolution. There are a growing number of DNA tools, some of which embody most of the SNA techniques.



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