📄 Motifs in Temporal Networks
Ashwin Paranjape, Austin R. Benson, Jure Leskovec
🔗 https://arxiv.org/pdf/1612.09259v1
📌 ABSTRACT
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs in temporal networks, which contain many timestamped links between the nodes, is not yet well understood.
Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of temporal edges, design fast algorithms for counting temporal motifs, and prove their runtime complexity. Our fast algorithms achieve up to 56.5x speedup compared to a baseline method. Furthermore, we use our algorithms to count temporal motifs in a variety of networks. Results show that networks from different domains have significantly different motif counts, whereas networks from the same domain tend to have similar motif counts. We also find that different motifs occur at different time scales, which provides further insights into structure and function of temporal networks.
Ashwin Paranjape, Austin R. Benson, Jure Leskovec
🔗 https://arxiv.org/pdf/1612.09259v1
📌 ABSTRACT
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs in temporal networks, which contain many timestamped links between the nodes, is not yet well understood.
Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of temporal edges, design fast algorithms for counting temporal motifs, and prove their runtime complexity. Our fast algorithms achieve up to 56.5x speedup compared to a baseline method. Furthermore, we use our algorithms to count temporal motifs in a variety of networks. Results show that networks from different domains have significantly different motif counts, whereas networks from the same domain tend to have similar motif counts. We also find that different motifs occur at different time scales, which provides further insights into structure and function of temporal networks.
🎯 2017 : WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?
https://www.edge.org/response-detail/27036
#networks
https://www.edge.org/response-detail/27036
#networks
🎯 My answer to the Edge 2017 annual question: What scientific concept should be better known?
#Criticality
In physics we say a system is in a critical state when it is ripe for a phase transition. Consider water turning into ice, or a cloud that is pregnant with rain. Both of these are examples of physical systems in a critical state.
The dynamics of criticality, however, are not very intuitive. Consider the abruptness of freezing water. For an outside observer, there is no difference between cold water and water that is just about to freeze. This is because water that is just about to freeze is still liquid. Yet, microscopically, cold water and water that is about to freeze are not the same.
When close to freezing, water is populated by gazillions of tiny ice crystals, crystals that are so small that water remains liquid. But this is water in a critical state, a state in which any additional freezing will result in these crystals touching each other, generating the solid mesh we know as ice. Yet, the ice crystals that formed during the transition are infinitesimal. They are just the last straw. So, freezing cannot be considered the result of these last crystals. They only represent the instability needed to trigger the transition; the real cause of the transition is the criticality of the state.
But why should anyone outside statistical physics care about criticality?
The reason is that history is full of individual narratives that maybe should be interpreted in terms of critical phenomena.
Did Rosa Parks start the civil rights movement? Or was the movement already running in the minds of those who had been promised equality and were instead handed discrimination? Was the collapse of Lehman Brothers an essential trigger for the Great Recession? Or was the financial system so critical that any disturbance could have made the trick?
As humans, we love individual narratives. We evolved to learn from stories and communicate almost exclusively in terms of them. But as Richard Feynman said repeatedly: The imagination of nature is often larger than that of man. So, maybe our obsession with individual narratives is nothing but a reflection of our limited imagination. Going forward we need to remember that systems often make individuals irrelevant. Just like none of your cells can claim to control your body, society also works in systemic ways.
So, the next time the house of cards collapses, remember to focus on why we were building a house of cards in the first place, instead of focusing on whether the last card was the queen of diamonds or a two of clubs.
🔗 https://facebook.com/story.php?story_fbid=10154355446216693&id=727621692
#Criticality
In physics we say a system is in a critical state when it is ripe for a phase transition. Consider water turning into ice, or a cloud that is pregnant with rain. Both of these are examples of physical systems in a critical state.
The dynamics of criticality, however, are not very intuitive. Consider the abruptness of freezing water. For an outside observer, there is no difference between cold water and water that is just about to freeze. This is because water that is just about to freeze is still liquid. Yet, microscopically, cold water and water that is about to freeze are not the same.
When close to freezing, water is populated by gazillions of tiny ice crystals, crystals that are so small that water remains liquid. But this is water in a critical state, a state in which any additional freezing will result in these crystals touching each other, generating the solid mesh we know as ice. Yet, the ice crystals that formed during the transition are infinitesimal. They are just the last straw. So, freezing cannot be considered the result of these last crystals. They only represent the instability needed to trigger the transition; the real cause of the transition is the criticality of the state.
But why should anyone outside statistical physics care about criticality?
The reason is that history is full of individual narratives that maybe should be interpreted in terms of critical phenomena.
Did Rosa Parks start the civil rights movement? Or was the movement already running in the minds of those who had been promised equality and were instead handed discrimination? Was the collapse of Lehman Brothers an essential trigger for the Great Recession? Or was the financial system so critical that any disturbance could have made the trick?
As humans, we love individual narratives. We evolved to learn from stories and communicate almost exclusively in terms of them. But as Richard Feynman said repeatedly: The imagination of nature is often larger than that of man. So, maybe our obsession with individual narratives is nothing but a reflection of our limited imagination. Going forward we need to remember that systems often make individuals irrelevant. Just like none of your cells can claim to control your body, society also works in systemic ways.
So, the next time the house of cards collapses, remember to focus on why we were building a house of cards in the first place, instead of focusing on whether the last card was the queen of diamonds or a two of clubs.
🔗 https://facebook.com/story.php?story_fbid=10154355446216693&id=727621692
📄 The Unfolding and Control of Network Cascades
Adilson E. Motter, Yang Yang
🔗 https://arxiv.org/pdf/1701.00578v1
📌 ABSTRACT
A characteristic property of networks is their ability to propagate influences, such as infectious diseases, behavioral changes, and failures. An especially important class of such contagious dynamics is that of cascading processes. These processes include, for example, cascading failures in infrastructure systems, extinctions cascades in ecological networks, and information cascades in social systems. In this review, we discuss recent progress and challenges associated with the modeling, prediction, detection, and control of cascades in networks.
Adilson E. Motter, Yang Yang
🔗 https://arxiv.org/pdf/1701.00578v1
📌 ABSTRACT
A characteristic property of networks is their ability to propagate influences, such as infectious diseases, behavioral changes, and failures. An especially important class of such contagious dynamics is that of cascading processes. These processes include, for example, cascading failures in infrastructure systems, extinctions cascades in ecological networks, and information cascades in social systems. In this review, we discuss recent progress and challenges associated with the modeling, prediction, detection, and control of cascades in networks.
🌀 Neural Networks for Machine Learning
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
🔗 https://www.coursera.org/learn/neural-networks?utm_medium=email&utm_source=marketing&utm_campaign=5Er1QNLaEeatnG9kVehcuw
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
🔗 https://www.coursera.org/learn/neural-networks?utm_medium=email&utm_source=marketing&utm_campaign=5Er1QNLaEeatnG9kVehcuw
Complex Systems Studies
https://www.amazon.com/Signals-Boundaries-Building-Complex-Adaptive/dp/0262525933/
🌀 Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other cas. Despite a wealth of data and descriptions concerning different cas, there remain many unanswered questions about "steering" these systems. In Signals and Boundaries, John Holland argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering cas through the mechanisms that generate their signal/boundary hierarchies.
Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.
Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.
Complex Systems Studies
https://www.amazon.com/Signals-Boundaries-Building-Complex-Adaptive/dp/0262525933/
A short, 2 page, review of the work by Chris Adami can be found here:
🔗 http://adamilab.msu.edu/wp-content/uploads/Adami2012c.pdf
🔗 http://adamilab.msu.edu/wp-content/uploads/Adami2012c.pdf
Forwarded from کتابخانه حسینیه ارشاد
برنامه هشتمين دوره سلسه نشست هاي علم اطلاعات و دانش شناسي در فصل زمستان 95
Complex Systems Studies
https://www.elsevier.com/books/the-synchronized-dynamics-of-complex-systems/boccaletti/978-0-444-52743-1
Table of Contents
Chapter 1 – Preface Chapter 2 – Introduction Chapter 3 – Identical Systems Chapter 4 – Non identical Systems Chapter 5 – Structurally non equivalent Systems Chapter 6 – Effects of noise Chapter 7 – Distributed and Extended Systems Chapter 8 – Complex Networks
Chapter 1 – Preface Chapter 2 – Introduction Chapter 3 – Identical Systems Chapter 4 – Non identical Systems Chapter 5 – Structurally non equivalent Systems Chapter 6 – Effects of noise Chapter 7 – Distributed and Extended Systems Chapter 8 – Complex Networks
🗞 Community detection, link prediction and layer interdependence in multilayer networks
Caterina De Bacco, Eleanor A. Power, Daniel B. Larremore, Cristopher Moore
🔗 https://arxiv.org/pdf/1701.01369v1
📌 ABSTRACT
Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information, and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substrings material between genes of the malaria parasite.
Caterina De Bacco, Eleanor A. Power, Daniel B. Larremore, Cristopher Moore
🔗 https://arxiv.org/pdf/1701.01369v1
📌 ABSTRACT
Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information, and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substrings material between genes of the malaria parasite.