Blockmodeling

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Blockmodeling is a set or a coherent framework, that is used for analyzing social structure and also for setting procedure(s) for partitioning (clustering) social network's units (nodes, vertices, actors), based on specific patterns, which form a distinctive structure through interconnectivity. [1] [2] It is primarily used in statistics, machine learning and network science.

Contents

As an empirical procedure, blockmodeling assumes that all the units in a specific network can be grouped together to such extent to which they are equivalent. Regarding equivalency, it can be structural, regular or generalized. [3] Using blockmodeling, a network can be analyzed using newly created blockmodels, which transforms large and complex network into a smaller and more comprehensible one. At the same time, the blockmodeling is used to operationalize social roles.

While some contend that the blockmodeling is just clustering methods, Bonacich and McConaghy state that "it is a theoretically grounded and algebraic approach to the analysis of the structure of relations". Blockmodeling's unique ability lies in the fact that it considers the structure not just as a set of direct relations, but also takes into account all other possible compound relations that are based on the direct ones. [4]

The principles of blockmodeling were first introduced by Francois Lorrain and Harrison C. White in 1971. [2] Blockmodeling is considered as "an important set of network analytic tools" as it deals with delineation of role structures (the well-defined places in social structures, also known as positions) and the discerning the fundamental structure of social networks. [5] : 2, 3  According to Batagelj, the primary "goal of blockmodeling is to reduce a large, potentially incoherent network to a smaller comprehensible structure that can be interpreted more readily". [6] Blockmodeling was at first used for analysis in sociometry and psychometrics, but has now spread also to other sciences. [7]

Definition

Different characteristics of social networks. A, B, and C show varying centrality and density of networks; panel D shows network closure, i.e., when two actors, tied to a common third actor, tend to also form a direct tie between them. Panel E represents two actors with different attributes (e.g., organizational affiliation, beliefs, gender, education) who tend to form ties. Panel F consists of two types of ties: friendship (solid line) and dislike (dashed line). In this case, two actors being friends both dislike a common third (or, similarly, two actors that dislike a common third tend to be friends). Social network characteristics diagram.jpg
Different characteristics of social networks. A, B, and C show varying centrality and density of networks; panel D shows network closure, i.e., when two actors, tied to a common third actor, tend to also form a direct tie between them. Panel E represents two actors with different attributes (e.g., organizational affiliation, beliefs, gender, education) who tend to form ties. Panel F consists of two types of ties: friendship (solid line) and dislike (dashed line). In this case, two actors being friends both dislike a common third (or, similarly, two actors that dislike a common third tend to be friends).

A network as a system is composed of (or defined by) two different sets: one set of units (nodes, vertices, actors) and one set of links between the units. Using both sets, it is possible to create a graph, describing the structure of the network. [8]

During blockmodeling, the researcher is faced with two problems: how to partition the units (e.g., how to determine the clusters (or classes), that then form vertices in a blockmodel) and then how to determine the links in the blockmodel (and at the same time the values of these links). [9]

In the social sciences, the networks are usually social networks, composed of several individuals (units) and selected social relationships among them (links). Real-world networks can be large and complex; blockmodeling is used to simplify them into smaller structures that can be easier to interpret. Specifically, blockmodeling partitions the units into clusters and then determines the ties among the clusters. At the same time, blockmodeling can be used to explain the social roles existing in the network, as it is assumed that the created cluster of units mimics (or is closely associated with) the units' social roles. [8]

In graph theory, the image provides a simplified view of a network, where each of the numbers represents a different node. 6n-graf.svg
In graph theory, the image provides a simplified view of a network, where each of the numbers represents a different node.

Blockmodeling can thus be defined as a set of approaches for partitioning units into clusters (also known as positions) and links into blocks, which are further defined by the newly obtained clusters. A block (also blockmodel) is defined as a submatrix, that shows interconnectivity (links) between nodes, present in the same or different clusters. [8] Each of these positions in the cluster is defined by a set of (in)direct ties to and from other social positions. [10] These links (connections) can be directed or undirected; there can be multiple links between the same pair of objects or they can have weights on them. If there are not any multiple links in a network, it is called a simple network. [11] : 8 

A matrix representation of a graph is composed of ordered units, in rows and columns, based on their names. The ordered units with similar patterns of links are partitioned together in the same clusters. Clusters are then arranged together so that units from the same clusters are placed next to each other, thus preserving interconnectivity. In the next step, the units (from the same clusters) are transformed into a blockmodel. With this, several blockmodels are usually formed, one being core cluster and others being cohesive; a core cluster is always connected to cohesive ones, while cohesive ones cannot be linked together. Clustering of nodes is based on the equivalence, such as structural and regular. [8] The primary objective of the matrix form is to visually present relations between the persons included in the cluster. These ties are coded dichotomously (as present or absent), and the rows in the matrix form indicate the source of the ties, while the columns represent the destination of the ties. [10]

Equivalence can have two basic approaches: the equivalent units have the same connection pattern to the same neighbors or these units have same or similar connection pattern to different neighbors. If the units are connected to the rest of network in identical ways, then they are structurally equivalent. [3] Units can also be regularly equivalent, when they are equivalently connected to equivalent others. [2]

With blockmodeling, it is necessary to consider the issue of results being affected by measurement errors in the initial stage of acquiring the data. [12]

Different approaches

Regarding what kind of network is undergoing blockmodeling, a different approach is necessary. Networks can be one–mode or two–mode. In the former all units can be connected to any other unit and where units are of the same type, while in the latter the units are connected only to the unit(s) of a different type. [5] : 6–10  Regarding relationships between units, they can be single–relational or multi–relational networks. Further more, the networks can be temporal or multilevel and also binary (only 0 and 1) or signed (allowing negative ties)/values (other values are possible) networks.

Different approaches to blockmodeling can be grouped into two main classes: deterministic blockmodeling and stochastic blockmodeling approaches. Deterministic blockmodeling is then further divided into direct and indirect blockmodeling approaches. [8]

Structural equivalence Structural Equivalence.jpg
Structural equivalence

Among direct blockmodeling approaches are: structural equivalence and regular equivalence. [2] Structural equivalence is a state, when units are connected to the rest of the network in an identical way(s), while regular equivalence occurs when units are equally related to equivalent others (units are not necessarily sharing neighbors, but have neighbour that are themselves similar). [3] [5] : 24 

Regular equivalence Regular equivalence.jpg
Regular equivalence

Indirect blockmodeling approaches, where partitioning is dealt with as a traditional cluster analysis problem (measuring (dis)similarity results in a (dis)similarity matrix), are: [8] [2]

According to Brusco and Steinley (2011), [14] the blockmodeling can be categorized (using a number of dimensions): [15]

Blockmodels

Blockmodels (sometimes also block models) are structures in which:

Computer programs can partition the social network according to pre-set conditions. [17] : 333  When empirical blocks can be reasonably approximated in terms of ideal blocks, such blockmodels can be reduced to a blockimage, which is a representation of the original network, capturing its underlying 'functional anatomy'. [18] Thus, blockmodels can "permit the data to characterize their own structure", and at the same time not seek to manifest a preconceived structure imposed by the researcher. [19]

Blockmodels can be created indirectly or directly, based on the construction of the criterion function. Indirect construction refers to a function, based on "compatible (dis)similarity measure between paris of units", while the direct construction is "a function measuring the fit of real blocks induced by a given clustering to the corresponding ideal blocks with perfect relations within each cluster and between clusters according to the considered types of connections (equivalence)". [20]

Types

Blockmodels can be specified regarding the intuition, substance or the insight into the nature of the studied network; this can result in such models as follows: [5] : 16–24 

Specialized programs

Blockmodeling is done with specialized computer programs, dedicated to the analysis of networks or blockmodeling in particular, as:

See also

Related Research Articles

<span class="mw-page-title-main">Vladimir Batagelj</span> Slovenian mathematician

Vladimir Batagelj is a Slovenian mathematician and an emeritus professor of mathematics at the University of Ljubljana. He is known for his work in discrete mathematics and combinatorial optimization, particularly analysis of social networks and other large networks (blockmodeling).

<span class="mw-page-title-main">Community structure</span> Concept in graph theory

In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into sets of nodes such that each set of nodes is densely connected internally. In the particular case of non-overlapping community finding, this implies that the network divides naturally into groups of nodes with dense connections internally and sparser connections between groups. But overlapping communities are also allowed. The more general definition is based on the principle that pairs of nodes are more likely to be connected if they are both members of the same community(ies), and less likely to be connected if they do not share communities. A related but different problem is community search, where the goal is to find a community that a certain vertex belongs to.

Social network analysis (SNA) software is software which facilitates quantitative or qualitative analysis of social networks, by describing features of a network either through numerical or visual representation.

Anuška Ferligoj is a Slovenian mathematician, born August 19, 1947, in Ljubljana, Slovenia, whose specialty is statistics and network analysis. Her specific interests include multivariate analysis, cluster analysis, social network analysis, methodological research of public opinion, analysis of scientific networks. She is Fellow of the European Academy of Sociology.

<span class="mw-page-title-main">Stochastic block model</span>

The stochastic block model is a generative model for random graphs. This model tends to produce graphs containing communities, subsets of nodes characterized by being connected with one another with particular edge densities. For example, edges may be more common within communities than between communities. Its mathematical formulation has been firstly introduced in 1983 in the field of social network by Paul W. Holland et al. The stochastic block model is important in statistics, machine learning, and network science, where it serves as a useful benchmark for the task of recovering community structure in graph data.

<span class="mw-page-title-main">Main path analysis</span> Mathematical tool

Main path analysis is a mathematical tool, first proposed by Hummon and Doreian in 1989, to identify the major paths in a citation network, which is one form of a directed acyclic graph (DAG). It has since become an effective technique for mapping technological trajectories, exploring scientific knowledge flows, and conducting literature reviews.

Aleš Žiberna is a Slovene statistician, whose specialty is network analysis. His specific research interests include blockmodeling, multivariate analysis and computer intensive methods.

In generalized blockmodeling, the blockmodeling is done by "the translation of an equivalence type into a set of permitted block types", which differs from the conventional blockmodeling, which is using the indirect approach. It's a special instance of the direct blockmodeling approach.

Patrick Doreian is an American mathematician and social scientist, whose specialty is network analysis. His specific research interests include blockmodeling, social structure and network processes.

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Deterministic blockmodeling is an approach in blockmodeling that does not assume a probabilistic model, and instead relies on the exact or approximate algorithms, which are used to find blockmodel(s). This approach typically minimizes some inconsistency that can occur with the ideal block structure. Such analysis is focused on clustering (grouping) of the network that is obtained with minimizing an objective function, which measures discrepancy from the ideal block structure.

Generalized blockmodeling of valued networks is an approach of the generalized blockmodeling, dealing with valued networks.

In mathematics applied to analysis of social structures, homogeneity blockmodeling is an approach in blockmodeling, which is best suited for a preliminary or main approach to valued networks, when a prior knowledge about these networks is not available. This is due to the fact, that homogeneity blockmodeling emphasizes the similarity of link (tie) strengths within the blocks over the pattern of links. In this approach, tie (link) values are assumed to be equal (homogenous) within blocks.

Blockmodeling linked networks is an approach in blockmodeling in analysing the linked networks. Such approach is based on the generalized multilevel blockmodeling approach. The main objective of this approach is to achieve clustering of the nodes from all involved sets, while at the same time using all available information. At the same time, all one-mode and two-node networks, that are connected, are blockmodeled, which results in obtaining only one clustering, using nodes from each sets. Each cluster ideally contains only nodes from one set, which also allows the modeling of the links among clusters from different sets. This approach was introduced by Aleš Žiberna in 2014.

Linked network in statistics is a network, which is composed of one-node networks, where the nodes from different one-node networks are connected through two-node networks. This means, that "linked networks are collections of networks defined on different sets of nodes", where all sets of nodes must be connected to each other.

<span class="mw-page-title-main">Signed network</span>

In a social network analysis, a positive or a negative 'friendship' can be established between two nodes in a network; this results in a signed network. As social interaction between people can be positive or negative, so can be links between the nodes.

Exploratory blockmodeling is an (inductive) approach in blockmodeling regarding the specification of an ideal blockmodel. This approach, also known as hypotheses-generating, is the simplest approach, as it "merely involves the definition of the block types permitted as well as of the number of clusters." With this approach, researcher usually defines the best possible blockmodel, which then represent the base for the analysis of the whole network.

Confirmatory blockmodeling is a deductive approach in blockmodeling, where a blockmodel is prespecify before the analysis, and then the analysis is fit to this model. When only a part of analysis is prespecify, it is called partially confirmatory blockmodeling.

Implicit blockmodeling is an approach in blockmodeling, similar to a valued and homogeneity blockmodeling, where initially an additional normalization is used and then while specifying the parameter of the relevant link is replaced by the block maximum.

Generalized blockmodeling of binary networks is an approach of generalized blockmodeling, analysing the binary network(s).

References

  1. ↑ Patrick Doreian, Positional Analysis and Blockmodeling. Encyclopedia of Complexity and Systems Science. DOI: https://doi.org/10.1007/978-0-387-30440-3_412 Archived 2023-02-04 at the Wayback Machine .
  2. 1 2 3 4 5 Patrick Doreian, An Intuitive Introduction to Blockmodeling with Examples, BMS: Bulletin of Sociological Methodology / Bulletin de Méthodologie Sociologique, January, 1999, No. 61 (January, 1999), pp. 5–34.
  3. 1 2 3 Anuška Ferligoj: Blockmodeling, http://mrvar.fdv.uni-lj.si/sola/info4/nusa/doc/blockmodeling-2.pdf Archived 2021-08-12 at the Wayback Machine
  4. ↑ Bonacich, Phillip; McConaghy, Maureen J. (1980). "The Algebra of Blockmodeling". Sociological Methodology. 11: 489–532. doi:10.2307/270873.
  5. 1 2 3 4 Doreian, Patrick; Batagelj, Vladimir; Ferligoj, Anuška (2005). Generalized Blockmodeling. Cambridge University Press. ISBN   0-521-84085-6.
  6. ↑ Batagelj, Vladimir (1999). "Generalized Blockmodeling". Informatica. 23: 501–506.
  7. ↑ "WEBER, M. (2007), "Introducing blockmodeling to input-output analysis". 16th International I-Ot Conf, Istanbul, Turkey". Archived from the original on 2021-08-23. Retrieved 2021-08-23.
  8. 1 2 3 4 5 6 7 Miha Matjašič, Marjan Cugmas and Aleš Žiberna, blockmodeling: An R package for generalized blockmodeling, Metodološki zvezki, 17(2), 2020, 49–66.
  9. ↑ Batagelj, Vladimir (1997). "Notes on blockmodeling". Social Networks. 19: 143–155.
  10. 1 2 Bonacich, Phillip; McConaghy, Maureen J. (1980). "The Algebra of Blockmodeling". Sociological Methodology. 11: 489–532. doi:10.2307/270873.
  11. ↑ Brian Joseph Ball, Blockmodeling techniques for complex networks: doctoral dissertation. University of Michigan, 2014.
  12. 1 2 Žnidaršič, Anja; Doreian, Patrick; Ferligoj, Anuška (2012). "Absent Ties in Social Networks, their Treatments, and Blockmodeling Outcomes". Metodološki zvezki. 9 (2): 119–138.
  13. ↑ Žiberna, Aleš (2013). "Generalized blockmodeling of sparse networks". Metodološki zvezki. 10 (2): 99–119.
  14. ↑ Brusco, Michael; Steinley, Douglas (2011). "A tabu search heuristic for deterministic two-mode blockmodeling". Psychometrika. 76: 612–633.
  15. ↑ Brusco, Michael; Doreian, Patrick; Steinley, Douglas; Satornino, Cinthia B. (2013). "Multiobjective blockmodeling for social network analysis". Psychometrika. 78 (3): 498–525. doi:10.1007/S11336-012-9313-1.
  16. ↑ Patrick Doreian, Positional Analysis and Blockmodeling. Encyclopedia of Complexity and Systems Science. DOI: https://doi.org/10.1007/978-0-387-30440-3_412 Archived 2023-02-04 at the Wayback Machine .
  17. ↑ Nooy, Wouter de; Mrvar, Andrej; Batagelj, Vladimir (2018). Exploratory Social Network Analysis with Pajek. Revised and Expanded Edition for Updated Software. Third Edition. Cambridge University Press. ISBN   978-1-108-47414-6.
  18. ↑ Nordlund, Carl (2019). "Direct blockmodeling of valued and binary networks: a dichotomization-free approach". Social Networks. 61: 128–143. arXiv: 1910.10484 . doi:10.1016/j.socnet.2019.10.004. S2CID   204838377.
  19. ↑ Arabie, Phipps; Boorman, Scott A.; Levitt, Paul R. (1978). "Constructing Blockmodels: How and Why". Journal of Mathematical Psychology. 17: 21–63. doi:10.2307/270873. JSTOR   270873.
  20. ↑ Batagelj, Vladimir; Mrvar, andrej; Ferligoj, Anuška; Doreian, Patrick (2004). "Generalized Blockmodeling with Pajek". Metodološki zvezki. 1 (2): 455–467. Archived from the original on 2022-03-22. Retrieved 2023-01-07.
  21. 1 2 "STATS.ox.ac.uk – Social Network Analysis". Archived from the original on 2021-08-18. Retrieved 2021-08-18.
  22. ↑ Steiber, Steven R. (1981). "Building better blockmodels: A non–hierarchical extension of CONCOR with applications to regression analysis". Mid–American Review of Sociology. VI: 17–40.
  23. 1 2 3 Batagelj, Vladimir; Mrvar, Andrej; Ferligoj, Anuška; Doreian, Patrick (2004). "Generalized Blockmodeling with Pajek". Metodološki zvezki. 1 (2): 455–467.
  24. ↑ Cran.R–project.org – Package 'blockmodeling' [ permanent dead link ]
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