74 citations · 355 across the 31 of their papers we have counts for
31 papers
Entropy of microcanonical finite-graph ensembles
Tatsuro Kawamoto
The entropy of random graph ensembles has gained widespread attention in the field of graph theory and network science. We consider microcanonical ensembles of simple graphs with p…
Finding community structure using the ordered random graph model
Masaki Ochi, Tatsuro Kawamoto
Visualization of the adjacency matrix enables us to capture macroscopic features of a network when the matrix elements are aligned properly. Community structure, a network consisti…
Consistency between ordering and clustering methods for graphs
Tatsuro Kawamoto, Masaki Ochi, Teruyoshi Kobayashi
A relational dataset is often analyzed by optimally assigning a label to each element through clustering or ordering. While similar characterizations of a dataset would be achieved…
Single-trajectory map equation
Tatsuro Kawamoto
Community detection, the process of identifying module structures in complex systems represented on networks, is an effective tool in various fields of science. The map equation, w…
Contribution of directedness in graph spectra
Masaki Ochi, Tatsuro Kawamoto
In graph analyses, directed edges are often approximated to undirected ones so that the adjacency matrices may be symmetric. However, such simplification has not been thoroughly ve…
Sequential locality of graphs and its hypothesis testing
Tatsuro Kawamoto, Teruyoshi Kobayashi
The adjacency matrix is the most fundamental and intuitive object in graph analysis that is useful not only mathematically but also for visualizing the structures of graphs. Becaus…
Graph-based open-ended survey on concerns related to COVID-19
Tatsuro Kawamoto, Takaaki Aoki, Michiko Ueda
The COVID-19 pandemic is an unprecedented public health crisis with broad social and economic consequences. We conducted four surveys between April and August 2020 using the graph-…
Identifying macroscopic features in foreign visitor travel pathways
Tatsuro Kawamoto, Ryutaro Hashimoto
Human travel patterns are commonly studied as networks in which the points of departure and destination are encoded as nodes and the travel frequency between two points is recorded…
Palette diagram: A Python package for visualization of collective categorical data
Chihiro Noguchi, Tatsuro Kawamoto
Categorical data, wherein a numerical quantity is assigned to each category (nominal variable), are ubiquitous in data science. A palette diagram is a visualization tool for a larg…
Spectral clustering of annotated graphs using a factor graph representation
Tatsuro Kawamoto
Graph-structured data commonly have node annotations. A popular approach for inference and learning involving annotated graphs is to incorporate annotations into a statistical mode…
Fragility of spectral clustering for networks with an overlapping structure
Chihiro Noguchi, Tatsuro Kawamoto
Communities commonly overlap in real-world networks. This is a motivation to develop overlapping community detection methods, because methods for non-overlapping communities may no…
Democratic summary of public opinions in free-response surveys
Tatsuro Kawamoto, Takaaki Aoki
Social surveys have been widely used as a method of obtaining public opinion. Sometimes it is more ideal to collect opinions by presenting questions in free-response formats than i…
Evaluating network partitions through visualization
Chihiro Noguchi, Tatsuro Kawamoto
Network clustering requires making many decisions manually, such as the number of groups and a statistical model to be used. Even after filtering using an information criterion or…
Mean-field theory of graph neural networks in graph partitioning
Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi
A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility t…
Counting the number of metastable states in the modularity landscape: Algorithmic detectability limit of greedy algorithms in community detection
Tatsuro Kawamoto, Yoshiyuki Kabashima
Modularity maximization using greedy algorithms continues to be a popular approach toward community detection in graphs, even after various better forming algorithms have been prop…
Algorithmic infeasibility of community detection in higher-order networks
Tatsuro Kawamoto
In principle, higher-order networks that have multiple edge types are more informative than their lower-order counterparts. In practice, however, excessively rich information may b…
Algorithmic detectability threshold of the stochastic block model
Tatsuro Kawamoto
The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the s…
Detectability thresholds of general modular graphs
Tatsuro Kawamoto, Yoshiyuki Kabashima
We investigate the detectability thresholds of various modular structures in the stochastic block model. Our analysis reveals how the detectability threshold is related to the deta…
Comparative analysis on the selection of number of clusters in community detection
Tatsuro Kawamoto, Yoshiyuki Kabashima
We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of f…
Cross-validation estimate of the number of clusters in a network
Tatsuro Kawamoto, Yoshiyuki Kabashima
Network science investigates methodologies that summarise relational data to obtain better interpretability. Identifying modular structures is a fundamental task, and assessment of…
A Tractable Fully Bayesian Method for the Stochastic Block Model
Kohei Hayashi, Takuya Konishi, Tatsuro Kawamoto
The stochastic block model (SBM) is a generative model revealing macroscopic structures in graphs. Bayesian methods are used for (i) cluster assignment inference and (ii) model sel…
Detectability of the spectral method for sparse graph partitioning
Tatsuro Kawamoto, Yoshiyuki Kabashima
We show that modularity maximization with the resolution parameter offers a unifying framework of graph partitioning. In this framework, we demonstrate that the spectral method exh…
Localized eigenvectors of the non-backtracking matrix
Tatsuro Kawamoto
In the case of graph partitioning, the emergence of localized eigenvectors can cause the standard spectral method to fail. To overcome this problem, the spectral method using a non…
Persistence of activity on Twitter triggered by a natural disaster: A data analysis
Tatsuro Kawamoto
In this note, we list the results of a simple analysis of a Twitter dataset: the complete dataset of Japanese tweets in the 1-week period after the Great East Japan earthquake, whi…