A Survey on Subgraph Counting

Ribeiro, Pedro; Paredes, Pedro; Silva, Miguel E P; Aparício, David; Silva, Fernando

doi:10.1145/3433652

Cited by 64 publications

(18 citation statements)

References 159 publications

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“…For example, a 6-node induced subgraph leads up to 112 different types of subgraphs and 407 different orbits. Taking link directions into consideration, there are 1, 530, 843 subgraphs and 9, 031, 113 orbits [35].…”

Section: B Motifs Vs Graphletsmentioning

confidence: 99%

A Network Science Perspective of Graph Convolutional Networks: A Survey

2023

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The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.

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Section: B Motifs Vs Graphletsmentioning

confidence: 99%

A Network Science Perspective of Graph Convolutional Networks: A Survey

2023

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“…To identify higher-order patterns, the spatial relatedness of objects can also be modeled as a network. Ribeiro et al summarize the field of sub-graph counting and motif discovery [71]. However, the number of cells (nodes in the graph) in combination with a variety of cell types (node attributes) renders current motif discovery algorithms computationally infeasible for interactive setups, especially when considering interaction at multiple scales.…”

Section: Related Workmentioning

confidence: 99%

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Warchol¹,

Krueger

Nirmal

et al. 2022

Preprint

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ThiNew multiplexed tissue imaging technologies have enabled the study of normal and diseased tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood networks and immune-tumor interactions.

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“…Graphlets is a similar, but slightly different method compared to our technique. Graplets are small ( n < 5) subgraphs of a larger graph through which the structure of larger graphs can be characterized [RPS∗21]. They have been used to gauge structural similarity in egocentric networks for visualization [HACH12].…”

Section: Background and Related Workmentioning

confidence: 99%

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

Sondag

Turkay

et al. 2022

Computer Graphics Forum

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Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex 'infection maps' of time-varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time-varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns. CCS Concepts• Applied computing → Health informatics; • Human-centered computing → Visualization design and evaluation methods; Visual analytics;

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A Survey on Subgraph Counting

Cited by 64 publications

References 159 publications

A Network Science Perspective of Graph Convolutional Networks: A Survey

A Network Science Perspective of Graph Convolutional Networks: A Survey

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

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