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Book ; Online: Flow Divergence

Blöcker, Christopher / Scholtes, Ingo

Comparing Maps of Flows with Relative Entropy

2024

Abstract: Networks represent how the entities of a system are connected and can be partitioned differently, prompting ways to compare partitions. Common approaches for comparing network partitions include information-theoretic measures based on mutual information ... ...

Abstract	Networks represent how the entities of a system are connected and can be partitioned differently, prompting ways to compare partitions. Common approaches for comparing network partitions include information-theoretic measures based on mutual information and set-theoretic measures such as the Jaccard index. These measures are often based on computing the agreement in terms of overlap between different partitions of the same set. However, they ignore link patterns which are essential for the organisation of networks. We propose flow divergence, an information-theoretic divergence measure for comparing network partitions, inspired by the ideas behind the Kullback-Leibler divergence and the map equation for community detection. Similar to the Kullback-Leibler divergence, flow divergence adopts a coding perspective and compares two network partitions $\mathsf{M}_a$ and $\mathsf{M}_b$ by considering the expected extra number of bits required to describe a random walk on a network using $\mathsf{M}_b$ relative to reference partition $\mathsf{M}_a$. Because flow divergence is based on random walks, it can be used to compare partitions with arbitrary and different depths. We show that flow divergence distinguishes between partitions that traditional measures consider to be equally good when compared to a reference partition. Applied to real networks, we use flow divergence to estimate the cost of overfitting in incomplete networks and to visualise the solution landscape of network partitions.
Keywords	Computer Science - Social and Information Networks ; Physics - Physics and Society
Subject code	006
Publishing date	2024-01-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping flows on bipartite networks.

Blöcker, Christopher / Rosvall, Martin

Physical review. E

2020 Volume 102, Issue 5-1, Page(s) 52305

Abstract: Mapping network flows provides insight into the organization of networks, but even though many real networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how can ...

Abstract	Mapping network flows provides insight into the organization of networks, but even though many real networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how can we use it to understand the structure of bipartite networks better? The map equation models network flows with a random walk and exploits the information-theoretic duality between compression and finding regularities to detect communities in networks. However, it does not use the fact that random walks in bipartite networks alternate between node types, information worth 1 bit. To make some or all of this information available to the map equation, we developed a coding scheme that remembers node types at different rates. We explored the community landscape of bipartite real-world networks from no node-type information to full node-type information and found that using node types at a higher rate generally leads to deeper community hierarchies and a higher resolution. The corresponding compression of network flows exceeds the amount of extra information provided. Consequently, taking advantage of the bipartite structure increases the resolution and reveals more network regularities.
Language	English
Publishing date	2020-11-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2844562-4
ISSN	2470-0053 ; 2470-0045
ISSN (online)	2470-0053
ISSN	2470-0045
DOI	10.1103/PhysRevE.102.052305
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Book ; Online: The Map Equation Goes Neural

Blöcker, Christopher / Tan, Chester / Scholtes, Ingo

2023

Abstract: Community detection and graph clustering are essential for unsupervised data exploration and understanding the high-level organisation of networked systems. Recently, graph clustering has received attention as a primary task for graph neural networks. ... ...

Abstract	Community detection and graph clustering are essential for unsupervised data exploration and understanding the high-level organisation of networked systems. Recently, graph clustering has received attention as a primary task for graph neural networks. Although hierarchical graph pooling has been shown to improve performance in graph and node classification tasks, it performs poorly in identifying meaningful clusters. Community detection has a long history in network science, but typically relies on optimising objective functions with custom-tailored search algorithms, not leveraging recent advances in deep learning, particularly from graph neural networks. In this paper, we narrow this gap between the deep learning and network science communities. We consider the map equation, an information-theoretic objective function for unsupervised community detection. Expressing it in a fully differentiable tensor form that produces soft cluster assignments, we optimise the map equation with deep learning through gradient descent. More specifically, the reformulated map equation is a loss function compatible with any graph neural network architecture, enabling flexible clustering and graph pooling that clusters both graph structure and data features in an end-to-end way, automatically finding an optimum number of clusters without explicit regularisation by following the minimum description length principle. We evaluate our approach experimentally using different neural network architectures for unsupervised clustering in synthetic and real data. Our results show that our approach achieves competitive performance against baselines, naturally detects overlapping communities, and avoids over-partitioning sparse graphs.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-10-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Similarity-based Link Prediction from Modular Compression of Network Flows

Blöcker, Christopher / Smiljanić, Jelena / Scholtes, Ingo / Rosvall, Martin

2022

Abstract: Node similarity scores are a foundation for machine learning in graphs for clustering, node classification, anomaly detection, and link prediction with applications in biological systems, information networks, and recommender systems. Recent works on ... ...

Abstract	Node similarity scores are a foundation for machine learning in graphs for clustering, node classification, anomaly detection, and link prediction with applications in biological systems, information networks, and recommender systems. Recent works on link prediction use vector space embeddings to calculate node similarities in undirected networks with good performance. Still, they have several disadvantages: limited interpretability, need for hyperparameter tuning, manual model fitting through dimensionality reduction, and poor performance from symmetric similarities in directed link prediction. We propose MapSim, an information-theoretic measure to assess node similarities based on modular compression of network flows. Unlike vector space embeddings, MapSim represents nodes in a discrete, non-metric space of communities and yields asymmetric similarities in an unsupervised fashion. We compare MapSim on a link prediction task to popular embedding-based algorithms across 47 networks and find that MapSim's average performance across all networks is more than 7% higher than its closest competitor, outperforming all embedding methods in 11 of the 47 networks. Our method demonstrates the potential of compression-based approaches in graph representation learning, with promising applications in other graph learning tasks. Comment: In: Proceedings of the First Learning on Graphs Conference, PMLR 198:52:1-52:18, 2022. Available at https://proceedings.mlr.press/v198/blocker22a.html
Keywords	Computer Science - Social and Information Networks ; Physics - Physics and Society
Subject code	006
Publishing date	2022-08-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Map Equation Centrality

Blöcker, Christopher / Nieves, Juan Carlos / Rosvall, Martin

Community-aware Centrality based on the Map Equation

2022

Abstract: To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, ...

Abstract	To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network's modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The information-theoretic centrality measure can be determined from a node's local network context alone because changes to the coding scheme only affect other nodes in the same module. Map equation centrality is agnostic to the chosen network flow model and allows researchers to select the model that best reflects the dynamics of the process under study. Applied to synthetic networks, we highlight how our approach enables a more fine-grained differentiation between nodes than node-local or network-global measures. Predicting influential nodes for two different dynamical processes on real-world networks with traditional and other community-aware centrality measures, we find that activating nodes based on map equation centrality scores tends to create the largest cascades in a linear threshold model.
Keywords	Computer Science - Social and Information Networks ; Physics - Physics and Society
Subject code	006
Publishing date	2022-01-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Mapping Flows on Bipartite Networks

Blöcker, Christopher / Rosvall, Martin

2020

Abstract: Mapping network flows provides insight into the organization of networks, but even though many real-networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how can ...

Abstract	Mapping network flows provides insight into the organization of networks, but even though many real-networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how can we use it to understand the structure of bipartite networks better? The map equation models network flows with a random walk and exploits the information-theoretic duality between compression and finding regularities to detect communities in networks. However, it does not use the fact that random walks in bipartite networks alternate between node types, information worth 1 bit. To make some or all of this information available to the map equation, we developed a coding scheme that remembers node types at different rates. We explored the community landscape of bipartite real-world networks from no node-type information to full node-type information and found that using node types at a higher rate generally leads to deeper community hierarchies and a higher resolution. The corresponding compression of network flows exceeds the amount of extra information provided. Consequently, taking advantage of the bipartite structure increases the resolution and reveals more network regularities.
Keywords	Computer Science - Social and Information Networks ; Physics - Physics and Society
Subject code	004
Publishing date	2020-07-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Community Detection with the Map Equation and Infomap

Smiljanić, Jelena / Blöcker, Christopher / Holmgren, Anton / Edler, Daniel / Neuman, Magnus / Rosvall, Martin

Theory and Applications

2023

Abstract: Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, ... ...

Abstract	Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, detecting community structures in complex networks requires selecting a community detection method among a multitude of alternatives with different network representations, community interpretations, and underlying mechanisms. This review and tutorial focuses on a popular community detection method called the map equation and its search algorithm Infomap. The map equation framework for community detection describes communities by analyzing dynamic processes on the network. Thanks to its flexibility, the map equation provides extensions that can incorporate various assumptions about network structure and dynamics. To help decide if the map equation is a suitable community detection method for a given complex system and problem at hand -- and which variant to choose -- we review the map equation's theoretical framework and guide users in applying the map equation to various research problems.
Keywords	Physics - Physics and Society ; Physics - Data Analysis ; Statistics and Probability
Subject code	306
Publishing date	2023-11-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Particle detectors: introduction to experimental particle physics.

Blocker, C

Science (New York, N.Y.)

1987 Volume 235, Issue 4792, Page(s) 1091b–2b

Language	English
Publishing date	1987-02-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	128410-1
ISSN	1095-9203 ; 0036-8075
ISSN (online)	1095-9203
ISSN	0036-8075
DOI	10.1126/science.235.4792.1091b
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Zs.A 27: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 77: Show issues

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Article ; Online: Enhancer-derived long non-coding RNAs CCAT1 and CCAT2 at rs6983267 has limited predictability for early stage colorectal carcinoma metastasis.

Thean, Lai Fun / Blöcker, Christopher / Li, Hui Hua / Lo, Michelle / Wong, Michelle / Tang, Choong Leong / Tan, Emile K W / Rozen, Steven G / Cheah, Peh Yean

Scientific reports

2021 Volume 11, Issue 1, Page(s) 404

Abstract: Up-regulation of long non-coding RNAs (lncRNAs), colon-cancer associated transcript (CCAT) 1 and 2, was associated with worse prognosis in colorectal cancer (CRC). Nevertheless, their role in predicting metastasis in early-stage CRC is unclear. We ... ...

Abstract	Up-regulation of long non-coding RNAs (lncRNAs), colon-cancer associated transcript (CCAT) 1 and 2, was associated with worse prognosis in colorectal cancer (CRC). Nevertheless, their role in predicting metastasis in early-stage CRC is unclear. We measured the expression of CCAT1, CCAT2 and their oncotarget, c-Myc, in 150 matched mucosa-tumour samples of early-stage microsatellite-stable Chinese CRC patients with definitive metastasis status by multiplex real-time RT-PCR assay. Expression of CCAT1, CCAT2 and c-Myc were significantly up-regulated in the tumours compared to matched mucosa (p < 0.0001). The expression of c-Myc in the tumours was significantly correlated to time to metastasis [hazard ratio = 1.47 (1.10-1.97)] and the risk genotype (GG) of rs6983267, located within CCAT2. Expression of c-Myc and CCAT2 in the tumour were also significantly up-regulated in metastasis-positive compared to metastasis-negative patients (p = 0.009 and p = 0.04 respectively). Nevertheless, integrating the expression of CCAT1 and CCAT2 by the Random Forest classifier did not improve the predictive values of ColoMet19, the mRNA-based predictor for metastasis previously developed on the same series of tumours. The role of these two lncRNAs is probably mitigated via their oncotarget, c-Myc, which was not ranked high enough previously to be included in ColoMet19.
MeSH term(s)	Adenocarcinoma/diagnosis ; Adenocarcinoma/genetics ; Adenocarcinoma/pathology ; Biomarkers, Tumor/genetics ; Case-Control Studies ; Colorectal Neoplasms/diagnosis ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/pathology ; Disease Progression ; Enhancer Elements, Genetic/genetics ; Gene Expression Regulation, Neoplastic ; Humans ; Neoplasm Metastasis ; Neoplasm Staging ; Polymorphism, Single Nucleotide ; Prognosis ; RNA, Long Noncoding/genetics
Chemical Substances	Biomarkers, Tumor ; CCAT1 long noncoding RNA, human ; RNA, Long Noncoding ; long non-coding RNA CCAT2, human
Language	English
Publishing date	2021-01-11
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-79906-7
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Article ; Online: Beyond fitness tracking: The use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research.

Lim, Weng Khong / Davila, Sonia / Teo, Jing Xian / Yang, Chengxi / Pua, Chee Jian / Blöcker, Christopher / Lim, Jing Quan / Ching, Jianhong / Yap, Jonathan Jiunn Liang / Tan, Swee Yaw / Sahlén, Anders / Chin, Calvin Woon-Loong / Teh, Bin Tean / Rozen, Steven G / Cook, Stuart Alexander / Yeo, Khung Keong / Tan, Patrick

PLoS biology

2018 Volume 16, Issue 2, Page(s) e2004285

Abstract: The use of consumer-grade wearables for purposes beyond fitness tracking has not been comprehensively explored. We generated and analyzed multidimensional data from 233 normal volunteers, integrating wearable data, lifestyle questionnaires, cardiac ... ...

Abstract	The use of consumer-grade wearables for purposes beyond fitness tracking has not been comprehensively explored. We generated and analyzed multidimensional data from 233 normal volunteers, integrating wearable data, lifestyle questionnaires, cardiac imaging, sphingolipid profiling, and multiple clinical-grade cardiovascular and metabolic disease markers. We show that subjects can be stratified into distinct clusters based on daily activity patterns and that these clusters are marked by distinct demographic and behavioral patterns. While resting heart rates (RHRs) performed better than step counts in being associated with cardiovascular and metabolic disease markers, step counts identified relationships between physical activity and cardiac remodeling, suggesting that wearable data may play a role in reducing overdiagnosis of cardiac hypertrophy or dilatation in active individuals. Wearable-derived activity levels can be used to identify known and novel activity-modulated sphingolipids that are in turn associated with insulin sensitivity. Our findings demonstrate the potential for wearables in biomedical research and personalized health.
MeSH term(s)	Adult ; Cardiomegaly/diagnosis ; Cardiovascular Physiological Phenomena ; Exercise ; Female ; Fitness Trackers ; Healthy Volunteers ; Heart Rate ; Humans ; Insulin Resistance ; Life Style ; Male ; Medical Overuse/prevention & control ; Middle Aged ; Sphingolipids/blood ; Surveys and Questionnaires ; Ventricular Remodeling
Chemical Substances	Sphingolipids
Language	English
Publishing date	2018-02-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2126776-5
ISSN	1545-7885 ; 1544-9173
ISSN (online)	1545-7885
ISSN	1544-9173
DOI	10.1371/journal.pbio.2004285
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bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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