Demo data transformation SNA Demo data transformation

Two mode networks Affiliation or membership network A set of actors, and a second set of events or activities to which the actors in the first set attend or belong. Dyadic two-mode network Relations in a two-mode network measure ties between the actors in one set and actors in a second set

Bipartite graph

Two mode data

Modality A data set that contains information about two types of social entities (e.g. person and organizations) is a two mode network Individuals, group, organization, community, institution, society Think of individual persons as being embedded in networks that embedded in networks that are embedded in networks

Two-mode data For example: - - Participants of the same project - Membership in the same organization - Attendance at the same meetings - Graduates of the same university Heuristics/ rule of thumb, approximate Given actor-by-event or actor-by-group data, we can always construct an actor-by-actor matrix by counting the number of events/groups that each pair of actors has in common. 

Affiliation Network Putting context into the network by showing connections to activities, companies, organizations, neighborhoods, etc. Bipartite graph: every edge joins two nodes belonging to different sets (actors vs. foci) Sue Bill Chess Club Band Affiliations or foci

Two-mode affiliation network and its one –mode projection Top row represents four teams and the bottom row represents the teams’ Members (e.g. co-authors on a paper or artists that make a show. Teammates are members of a fully linked clique (e.g. ABC, BCD, CE, and DF) Connections form between agents on separate teams when links like (BC) connect The ABC, BCD, and CE teams. Most social networks are conceived of as relations among a set of nodes, and therefore represented as a 1-mode matrix (typically of 1s and 0s) or a simple graph or digraph. For example, we might collect data on who is friendly with whom within an organization, or who injects drugs with whom in a neighborhood. However, 2-mode data are common in social network contexts as well. Typical examples include, actor-by-event attendance (as in the DGG data), actor by group membership (such as managers sitting on corporate boards), and actor by trait possession (such as adjective checklist data), and actor by object possession (such as material style of life scales in which inventories are made of household possessions). In many cases when 2-mode data are collected, the analytical interest is focused on one mode or the other. For example, in the DGG dataset, person-by-event attendances were collected in order to understand social relations among the women, specifically, whether women tended to have social relations primarily within their own social classes. In the interlocking directorate literature, membership of executives on corporate boards is collected mainly in order to understand how corporations are intertwined, and how the structure of this connectivity affects corporate control of society. However, it can also occur that neither mode dominates our analytical focus and the primary interest is in the correspondence of one mode to the other. For example, a university might ask its faculty which courses they prefer to teach. Here, the objective is typically not to understand how faculty are related to each other through courses, nor how courses are related via faculty, but in the optimal assignments of persons to courses so that courses are staffed and faculty are not complaining.

Data>ntu _100_2mode NetDraw>File>Open>UCINET>Two Mode

From two mode to one mode Data>Affiliations Dots stand for thesis committees a, b, c stand for faculty members Find screenshots from the online book

Two mode-one mode with Gephi Data source Node list 2 Edge list 2

Two mode transformation with Gephi

Two mode transformation with Gephi

Members vs. institutes

Two mode transformation with Gephi Save into a Gephi file before transformation

Two mode transformation with Gephi

Two mode transformation with Gephi

Two mode transformation with Gephi