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2. Adjacency matrices. These are square matrices for which the number
of rows, and hence of columns, is equal to the number of nodes. The
cell (i,j) contains a 1 if node i is connected to node j and a 0 if this is
not the case.
3. Adjacency lists. This is just a list of pairs (i,j) where only pairs that are
linked to each other are mentioned. This representation is very useful
if the matrix contains many zeros.
Examples of two-dimensional representations and their corresponding
adjacency matrices can be found in Fig. 10.1 and Table 10.2.
If j is a node then the nodes linked to j are called the neighbors of j.

10.1.2 Social Networks
When networks deal directly or indirectly with persons, such as authors,
friends (directly), or their papers (indirectly) one uses the term social net-
works and their study is then referred to as social network analysis (in
short: SNA), see Scott (1991), Wasserman and Faust (1994), and Otte and
Rousseau (2002). In social network analysis, one makes a distinction
between two types of analysis: the study of ego-networks, and global
studies. Ego-networks study the network originating from one actor
referred to as the ego. The ego network is the subgraph consisting of the
ego and all nodes linked directly (neighbors) or indirectly (neighbors of
neighbors) to the ego, and the corresponding links. Sometimes an ego
network is restricted to the ego and its neighbors. An example in the
information sciences is Howard White’s description of Eugene Garfield’s
research network (White, 2000). When performing a global analysis, one
tries to map all relations of all actors belonging to the network.

Figure 10.1 Three simple networks with five nodes: (A), (B), and (C), from left to
right.