Michael J Brusco
Biographic Data
| ID | 636066 |
|---|---|
| NAME | Michael J Brusco |
| GIVEN NAMES | Michael J |
| FAMILY NAME | Brusco |
| SIGNATURE | BRUSCO M J |
| AFFILIATIONS | Florida State University |
| ORCID | 0000-0002-1465-6233 |
| VERIFIED | Yes |
| TOTAL WORKS | 20 |
| TOTAL CITATIONS | 18 |
| AUTHOR COUNT | 20 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 3 |
Improving the Walktrap Algorithm Using K -Means Clustering
The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encount…
Teaching the Mechanics of Factor Analysis Using Excel Spreadsheets
Combinatorial Optimization of Clustering Decisions
Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is …
On Fixed Marginal Distributions and Psychometric Network Models
This reply addresses the commentary by Epskamp et al. (in press) on our prior work, of using fixed marginals for sampling the data for testing hypothesis in psychometric network application. Mathematical results are presented for expected column (e.g., item prevalence) and row (e.g., subject severity) probabilities under three classical sampling schemes in categorical data analysis: (i) fixing the density, (ii) fixing either the row or column mar…
Detecting Clusters/Communities in Social Networks
Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this …
Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL -Median Heuristic
Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL -median heuristic (TMKLMedH) seeks to optimize the same criter…
Partitioning signed networks using relocation heuristics, tabu search, and variable neighborhood search
Local Optima in Mixture Modeling
It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are p…
Psychometrics
Using Cohen's κ for Community Detection in Social Networks
"Using Cohen's κ for Community Detection in Social Networks." Multivariate Behavioral Research, 50(6), pp. 740–741
A real-coded genetic algorithm for two-mode KL-means partitioning with application to homogeneity blockmodeling
A note on using the adjusted Rand index for link prediction in networks
An Exact Algorithm for Blockmodeling of Two-Mode Network Data
We consider problems where relationships between two sets (or modes) of objects are available in the form of a binary matrix with elements of 1 (0) indicating a bond (lack of a bond) between corresponding row and column objects. The goal is to establish a partition of the row objects and, simultaneously, a partition of the column objects to form blocks that consist of either exclusively 1s or exclusively 0s to the greatest extent possible. This t…
A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis
This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining …
Principal Cluster Axes
A measure of "clusterability" serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Compa…
Analysis of two-mode network data using nonnegative matrix factorization
Two Algorithms for Relaxed Structural Balance Partitioning
Understanding social phenomena with the help of mathematical models requires a coherent combination of theory, models, and data together with using valid data analytic methods. The study of social networks through the use of mathematical models is no exception. The intuitions of structural balance were formalized and led to a pair of remarkable theorems giving the nature of partition structures for balanced signed networks. Algorithms for partiti…
An exact algorithm for a core/periphery bipartitioning problem
A New Variable Weighting and Selection Procedure for K -means Cluster Analysis
A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these procedures are demonstrated in a simulation study, showing favorable results when compared with existin…
Cautionary Remarks on the Use of Clusterwise Regression
Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the within-cluster regression models from the error explained by the clustering process. In some cases, most of the …
A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis
This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining …
An Exact Algorithm for Blockmodeling of Two-Mode Network Data
We consider problems where relationships between two sets (or modes) of objects are available in the form of a binary matrix with elements of 1 (0) indicating a bond (lack of a bond) between corresponding row and column objects. The goal is to establish a partition of the row objects and, simultaneously, a partition of the column objects to form blocks that consist of either exclusively 1s or exclusively 0s to the greatest extent possible. This t…
Two Algorithms for Relaxed Structural Balance Partitioning
Understanding social phenomena with the help of mathematical models requires a coherent combination of theory, models, and data together with using valid data analytic methods. The study of social networks through the use of mathematical models is no exception. The intuitions of structural balance were formalized and led to a pair of remarkable theorems giving the nature of partition structures for balanced signed networks. Algorithms for partiti…
Partitioning signed networks using relocation heuristics, tabu search, and variable neighborhood search
Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL -Median Heuristic
Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL -median heuristic (TMKLMedH) seeks to optimize the same criter…
A note on using the adjusted Rand index for link prediction in networks
A New Variable Weighting and Selection Procedure for K -means Cluster Analysis
A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these procedures are demonstrated in a simulation study, showing favorable results when compared with existin…
Cautionary Remarks on the Use of Clusterwise Regression
Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the within-cluster regression models from the error explained by the clustering process. In some cases, most of the …
An exact algorithm for a core/periphery bipartitioning problem
Analysis of two-mode network data using nonnegative matrix factorization
Two Algorithms for Relaxed Structural Balance Partitioning
Understanding social phenomena with the help of mathematical models requires a coherent combination of theory, models, and data together with using valid data analytic methods. The study of social networks through the use of mathematical models is no exception. The intuitions of structural balance were formalized and led to a pair of remarkable theorems giving the nature of partition structures for balanced signed networks. Algorithms for partiti…
Principal Cluster Axes
A measure of "clusterability" serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Compa…
An Exact Algorithm for Blockmodeling of Two-Mode Network Data
We consider problems where relationships between two sets (or modes) of objects are available in the form of a binary matrix with elements of 1 (0) indicating a bond (lack of a bond) between corresponding row and column objects. The goal is to establish a partition of the row objects and, simultaneously, a partition of the column objects to form blocks that consist of either exclusively 1s or exclusively 0s to the greatest extent possible. This t…
A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis
This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining …
Psychometrics
Using Cohen's κ for Community Detection in Social Networks
"Using Cohen's κ for Community Detection in Social Networks." Multivariate Behavioral Research, 50(6), pp. 740–741
A real-coded genetic algorithm for two-mode KL-means partitioning with application to homogeneity blockmodeling
A note on using the adjusted Rand index for link prediction in networks
Local Optima in Mixture Modeling
It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are p…
Detecting Clusters/Communities in Social Networks
Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this …
Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL -Median Heuristic
Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL -median heuristic (TMKLMedH) seeks to optimize the same criter…
Partitioning signed networks using relocation heuristics, tabu search, and variable neighborhood search
Combinatorial Optimization of Clustering Decisions
Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is …
On Fixed Marginal Distributions and Psychometric Network Models
This reply addresses the commentary by Epskamp et al. (in press) on our prior work, of using fixed marginals for sampling the data for testing hypothesis in psychometric network application. Mathematical results are presented for expected column (e.g., item prevalence) and row (e.g., subject severity) probabilities under three classical sampling schemes in categorical data analysis: (i) fixing the density, (ii) fixing either the row or column mar…
Improving the Walktrap Algorithm Using K -Means Clustering
The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encount…
Teaching the Mechanics of Factor Analysis Using Excel Spreadsheets
Computer Science (20 works) · Mathematics (18 works) · Artificial Intelligence (12 works) · Algorithm (11 works) · Complex Network Analysis Techniques (10 works) · Data mining (10 works) · Artificial Intelligence (7 works) · Statistics (7 works) · Cluster analysis (6 works) · Machine learning (6 works)