KEYNOTE/INVITED SPEAKERS (in order of appearance)
“The Age of Information and the Fitness of Scientific Ideas and Inventions”
Though science’s knowledge base is expanding rapidly, the breakthrough paper rate is narrowing and scientists take longer to make their first discoveries. Breakthroughs are related to how information is recombined, yet it remains unclear how scientists and inventors forage the knowledge base in search of tomorrow’s highest impact ideas. Studying 28 million scientific papers and 5 million U.S. patents, we uncover 2 major findings. First, we identify “Darwin’s Conjecture,” which reveals how conventional and novel ideas are balanced within breakthrough papers. Second, we find an “information hotspot.” The hotspot is that cluster of papers of a certain age distribution in the knowledge base that best predict tomorrow’s hits. Together, works that combine knowledge according to Darwin’s Conjecture or forage in the hotspot double their odds of being in the top 5% or better of citations. These patterns result in over 250 scientific and technology fields, are increasingly dominant, and outperform other predictors of impact, suggesting a universal link between the age of information and scientific discovery.
“Collective Attention on the Web”
It’s one of the most popular online videos ever produced, having been viewed more than 800 million times on YouTube. At first glance, it’s hard to understand why the clip is so famous, since nothing much happens. Two little boys, Charlie and Harry, are sitting in a chair when Charlie, the younger brother, mischievously bites Harry’s finger. There’s a shriek and then a laugh. The clip is called “Charlie Bit My Finger—Again!” Why has this footage gone viral? How viral is it actually? Generally, understanding the dynamics of collective attention is central to an information where millions of people leave digital footprints everyday. We therefor have developed novel computational methods to characterize, analyze and even predict the dynamics of collective attention among millions of users to and within social media services. For instance, using mathematical epidemiology, we find that so-called „viral“ videos indeed show very high infection rates and, hence, should be called viral.
Based on joint works with Christian Bauckhage, Fabian Hadiji, and Rastegarpanah.
“Multilayer Networks and Applications”
One of the most active areas of network science, with an explosion of publications during the last few years, is the study of “multilayer networks,” in which heterogeneous types of entities can be connected via multiple types of ties that change in time. Multilayer networks can include multiple subsystems and “layers” of connectivity, and it is important to take multilayer features into account to try to improve our understanding of complex systems. In this talk, I’ll give an introduction to multilayer networks and will discuss applications in areas such as transportation, finance, neuroscience, and ecology.
“Hierarchies of order and order of hierarchies”
The word hierarchy comes from two Greek words hieros (holy) and arkhia (rule) and the concept appeared for the first time in the sixth century as the order of sacred things in Christian theology. Currently the word posseses many different meanings, inter alia: (i) order, i.e. a rank of any objects according to a certain parameter; (ii) relationship of control or domination; (iii) relationship of inclusion; (iv) coexistence of multiple organization levels. Complexity science is intersted in various hierarchies due to universal power laws (eg. Zipf law, Pareto distributions) being a sign of scale invariance and because of self-organization processes in multi-level physical, biological and social systems. Several hierarchical systems will be presented during the lecture. In the first case the evolution starts from a root node and the growth process is driven by rules of tournament selection. A system can be conceived as an evolving tree with a new node being attached to a contestant node at the best hierarchy level (a level nearest to the tree root). The proposed evolution reflects limited information on system properties available to new nodes. The information restrains the emergence of new hierarchy levels.In the second case the evolution starts from a bottom hierarchy level and then next levels are emerging. Therein, two dynamical processes are accounted for: agents’ promotions to next hierarchy levels and degradations to the lowest one. Following the initial stage of evolution the system approaches a stationary state where hierarchies no longer emerge and the distribution of agents at different levels is exponential. The average hierarchy level, the number of links per node, and the fraction of agents at the lowest level are all independent from the system size. However, the maximal hierarchy level grows logarithmically along the number of nodes. Computer simulations of opinion dynamics in hierarchical social groups and co-evolution of hierachical adaptive random Boolean networks will be demonstrated.
“Privacy in networks: Data mining as foe or friend?”
Online networks are great places for sharing data, discovering new knowledge in these data, and acting on this knowledge. Data mining plays a central role in these knowledge-based operations. But who profits, and who may be harmed? One widespread view is that data mining in networks can be instrumental for severe privacy violations. On the other hand, data mining is also expected to be able to empower users. In this talk, I report on our recent studies on (a) helping users manage their communication environment in online social networks and (b) analysing commercial tracking beyond advertising. I consider the applicable notions of networks, the concepts of privacy that is harmed or protected, and the role of data mining. I show how data mining can be a useful building block, but also needs to be extended by more systemic methods such as teaching approaches, in order to empower citizens.
“Networks Everywhere: On Construction of Semi-Structured Heterogeneous Networks from Massive Text Data”
The real-world big data are largely unstructured but interconnected, mainly in the form of natural language text. One of the grand challenges is to turn such massive data into actionable knowledge. In order to turn such massive unstructured, text-rich, but interconnected data into knowledge, we propose a D2N2K (i.e., data-to-network-to-knowledge) paradigm, that is, first turn data into relatively structured heterogeneous information networks, and then mine such text-rich and structure-rich heterogeneous networks to generate useful knowledge. We show why such a paradigm represents a promising direction and present some recent progress on the development of effective methods for construction and mining of structured heterogeneous information networks from text data. We argue that network science is the key at turning massive unstructured data into structured knowledge.
“Community structure in complex networks: genesis, graph spectra and algorithm validation”
Real networks display a modular organization, where modules, or communities, appear as subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network. In this talk I will show that communities emerge naturally in growing network models favoring triadic closure, a mechanism necessary to implement for the generation of large classes of systems, like e.g. social networks. I will show that the number of communities can be inferred by perturbing the adjacency matrix and see how its eigenvectors rotate. Finally I will address the crucial issue of validation, probably the single most important issue of network community detection. If using artificial benchmark graphs could bias methods towards the definition of community implemented by the benchmarks, real networks with metadata may or may not be useful for testing, contrary to general expectations.
“Cascading Overload Failures in Networks with Distributed Flows”
In complex information or infrastructure networks, even small localized disruptions can give rise to congestion, large-scale correlated failures, or cascades, — a critical vulnerability of such networks. Here, we study cascades of overload failures for distributed flows in spatial and non-spatial random graphs, and empirical networks. Our recent results on load-based cascading failures in spatially-embedded random networks (applicable to power grids) underline both the conceptual and computational challenges and difficulties identifying critical nodes, lines, and regions to mitigate cascades. We observed that cascading failures are non-self-averaging in spatial graphs, hence predictability is poor and conventional mitigation strategies are largely ineffective. Among our main findings is that protecting all nodes (or edges) by the same additional capacity (tolerance) may actually lead to larger global failures. I.e., indiscriminately investing resources in the protection of nodes or links can actually make the network more vulnerable against cascading failures (“paying more can result in less”, in terms of robustness).
“Fold networks as engines of generative tension”
The main mechanisms governing social tie formation and operation are at odds with recognizing new ideas. Homophily, closure, skewed degree distributions, and limited vision are four main forces of network gravity. This talk brings cases where these gravity forces were overcome by organizational design and emergent institutions. Using data on more than a hundred thousand video game developers from the 1980’s to the present, four hundred thousand jazz musicians from 1890 to the present, and the emergence of post-socialist business groups in Hungary I show mechanisms of achieving generative tension, productive diversity, and sustained exploration. I will highlight the role of structural folds, and the significance of overlapping yet cognitively diverse communities. Fold networks enable diverse associations, the recognition and realization of novelty. Fold networks also de-stabilize clusters and contribute to the constant renewal of associations.
“Utilizing Social Network Structures for Opportunistic Routing in MANETs”
Opportunistic routing (OR) is a new network routing paradigm for mobile, intermittently connected wireless networks. Unlike traditional routing, OR exploits node mobility to physically carry the data and forward it opportunistically when in contact with other nodes. A key challenge in OR is to determine the appropriate relays in order to minimize the number of copies forwarded, while maintaining message delivery time short. Since node mobility patterns are highly volatile and hard to control, attempts at exploiting stable social network structure for data forwarding have emerged. State-of-the-art OR protocols utilize a wide variety of social features for routing such as the friendship relationship, egocentric centrality, social similarity, social map, social contexts, and community structure. These heuristic-based social forwarding approaches achieve throughput efficiency and fairness in large scale, complex social networks, where connections among nodes follow a fat-tailed distribution. In this talk, we present recent developments in social-based OR protocols, discuss their pros and cons, and point out directions for future research.
“Cross-lingual Event Observatory”
Everything is interconnected, we are interacting on different levels, we co-exist in the same global environment, interdepend on each other which gives us opportunity to share and to develop mutual understanding. Electronic media, via physical network of devices and electronic interconnections between them, is making it very tangible. Crossing barriers between different countries, cultures, languages, life styles etc. is today a part of normal life. We cannot pretend it is not happening thus we suggest to put efforts in becoming more aware of the environment we all live in via globally observing events. Focusing on the events being reported about in electronic media, we see several challenges that motive research on data analytics. To contribute to that end, we have developed a system http://EventRegistry.org for observing events around the world in real-time via collecting information from over 300,000 news and social media sources. The underlying methods for text mining and data analytics enable handling large amount of textual data across different languages, enriching data with additional information, extracting information and identifying events, streaming information about events in open data formats, organizing and representing events to support observing global social dynamics. The talk will discuss challenges and research/technical solutions.
“Positional Dominance, Rankings and Centrality in Networks”
Centrality indices are used to assess the structural importance of nodes in a network, and they are commonly defined as graph invariants. Many such indices have been proposed, but there is no unifying theory of centrality. Axiomatic characterizations have focused on properties of particular indices, and conceptual frameworks are not suitable for mathematical treatment. A unifying framework for the concept of centrality is derived from a recently introduced positional approach to network science. It highlights a property shared by all standard centrality indices, preservation of the neighborhood-inclusion preorder, and provides ample opportunities for both, formal and empirical studies of centrality.
“Taming Complexity: Controlling Networks”
The ultimate proof of our understanding of biological or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered and natural systems towards a desired state, we lack a framework to control complex self-organized systems. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes whose time-dependent control can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but dense and homogeneous networks can be controlled via a few driver nodes. Counter-intuitively, we find that in both model and real systems the driver nodes tend to avoid the hubs.
Presentations in Parallel Sessions:
Long papers: 15 min presentation + 5 min questions/discussion
Oral presentations/short papers: 12 min presentation + 4 min questions/discussion
YRNCS Job Fair: MONDAY January 11th, 19:00-20:30, CONFERENCE ROOM “B”
The Young Researchers on Complex Systems (www.yrncs.com) organizes the YRNCS Job Fair.
YRNCS is an association, within CSS, of young researchers working in Complex Systems, whose aim is to foster interdisciplinary exchanges and build the basis of future collaborations.
The YRNCS Job Fair is targeted towards Master/PhD students and early postdocs in order to make the information on career openings circulate in the frame of NetSciX’16 and facilitate the access to such positions:
• If you look for candidates: you will be given the possibility to deliver a brief presentation of both the position, and the laboratory or group hosting it.
• If you look for positions: The Job Fair is the perfect frame for meeting potential hirers face to face prior to the application, explaining the specs of the opening.
Please send an email to email@example.com to show interest or ask further info.
If you have an opening please contact us and fill the form: http://www.yrncs.com/jobfair-netscix.
POSTER SESSION: Monday, January 11th, 17:00 – 19:00, FOYER
Please, consider that the posters will be displayed in one session scheduled for Monday 11th. Thank you all for the effort and the quality of contributions.
Network theory – general
|1||Grzegorz Siudem and Grzegorz Świątek. Diagonal Stationary Points of the Bethe Functional|
|2||Piotr Górski, Agnieszka Czaplicka and Janusz Hołyst. Coevolution in Hierarchical Adaptive Random Boolean Networks|
|3||Tomasz Kajdanowicz and Mikolaj Morzy. On Comparing Empirical and Theoretical Networks Using Entropy|
|4||Stephanie Keller-Schmidt, Murat Tugrul, Victor M. Eguiluz, Emilio Hernandez-Garcia and Konstantin Klemm. Age-dependent branching as a model of evolutionary trees|
|5||Baruch Barzel. Peeking into the Black Box: Reverse Engineering the Dynamics of Complex Systems|
|6||Stanislaw Saganowski, Piotr Bródka, Przemysław Kazienko and Tomasz Kajdanowicz. Looking Far Into the Social Groups’ Future|
|7||Robert Paluch, Krzysztof Suchecki and Janusz Holyst. Models of random graph hierarchies|
|8||Mahdi Jalili. Enhancing Pinning Controllability of Complex Networks through Link Rewiring|
|9||Marcin Blachnik, Karol Wawrzyniak, Marcin Jakubek and Michał Kłos. Dividing Electrical Grid into Zones Preserving Control Area Constrains|
|10||Josef Daňa. T-Shaped professional concept as an optimal knowledge network stabilizing team structure and project environment|
|11||Janusz Miśkiewicz. Network analysis of cross-correlations in econophysics.|
|12||Samin Aref and Mark C. Wilson. Measuring Partial Balance in Signed Networks|
|13||Divyesh Chandra Pisupati and Damir Vukicevic. Detecting outliers in Social Networks|
|14||Robert Kłopotek. Modeling bimodal social networks subject to recommendation|
|15||Xerxes Arsiwalla and Paul Verschure. Highly Integrated Information Near the Edge of Criticality is Universal Across Network Topologies|
Network theory – communities
|16||Jeremi Ochab. Reinventing the Triangles: Rule of Thumb for Assessing Detectability|
|17||Jarosław Walędziak and Krzysztof Trojanowski. Force-directed method for graph partitioning|
|18||Yohei Sakamoto, Yuichi Ikeda and Hideaki Aoyama. Physical model simulation reveals economic structural change.|
|19||Leila Hedayatifar, Forough Hassanibesheli, Amir Hossein Shirazi, Soheil Vasheghani Farahani and Gholamreza Jafari. The participation role of individuals in the emergence of pseudo-stable states in networks|
|20||Pascal Held and Rudolf Kruse. Online Community Detection by Using Nearest Hubs|
|21||Łukasz Rokita and Krzysztof Trojanowski. Louvain Method with Sorted Lists of Nodes for Network Partitioning|
Network theory – multi-layered networks
|22||Matteo Magnani and Luca Rossi. Network simplification strategies for multi-layer networks|
|23||Tomasz Kajdanowicz. Node Classification in Multiplex Networks using Random Walks|
|24||Adrian Popiel, Tomasz Kajdanowicz and Przemysław Kazienko. Local methods of collective classification in multiplex networks|
|25||Mieczysław Kłopotek, Slawomir T. Wierzchon and Robert Kłopotek. Network Capacity Bound for Personalized PageRank in Multimodal Networks|
Dynamical processes on networks
|26||Arkadiusz Jędrzejewski and Katarzyna Sznajd-Weron. Rethinking the person-situation approach – towards a more realistic model|
|27||Inia Steinbach, Jason Bassett, Thomas Isele, Vitaly Belik, Andreas Koher, Hartmut H. K. Lentz, Jörn Gethmann and Philipp Hövel. Agent-based modeling of epidemics in networks of livestock trade|
|28||Arkadiusz Jędrzejewski, Katarzyna Sznajd-Weron and Janusz Szwabinski. Mapping the $q$-voter model: From a single chain to complex networks|
|29||Jarosław Jankowski, Tomasz Kajdanowicz, Piotr Bródka, Radosław Michalski and Przemysław Kazienko. Sequential Seeding in Social Networks|
|30||Katarzyna Sznajd-Weron and Tomasz Weron. The size of the group matters!|
|31||Dariusz Krol. Measuring Information Spreading in Social Media|
|32||Andreas Koher, Hartmut H. K. Lentz, Philipp Hövel and Igor M. Sokolov. Infections on Temporal Networks – A Matrix-Based Approach|
|33||Alom Sela and Irad Ben-Gal. Message Struggle in a Multi-Opinion System|
|34||Anna Kowalska-Pyzalska, Katarzyna Maciejowska and Arkadiusz Jędrzejewski. Impact of word-of-mouth on social welfare: an agent-based modeling approach|
|35||Katarzyna Maciejowska and Anna Kowalska-Pyzalska. Optimal pricing in social networks: a perspective of the inovation diffusion process|
|36||Piotr Nyczka and Andrzej Jarynowski. Dynamics of marriage/divorces in changing environment|
|37||Janos Török, Zhongyuan Ruan and Janos Kertész. Collapse of a social network site: cascade behavior|
|38||Tomasz Ryczkowski, Agata Fronczak, Piotr Fronczak and Anna Chmiel. Mixed-order phase transitions in social networks: Exponential random graph approach|
|39||Nanxin Wei, Bo Fan and Gunnar Pruessner. Steady State of Cascade-Repair Dynamics on Random Networks|
|40||Andrzej Krawiecki and Tomasz Gradowski. Majority vote model on scale-free networks|
|41||Joanna Toruniewska, Krzysztof Suchecki and Janusz Hołyst. Co-evolution of the Potts model and topology of interactions|
|42||Adrianna Kozierkiewicz-Hetmańska. The analysis of quality of consensus determined for Big Data|
|43||Thomas Chesney. The Cascade Capacity Predicts Individuals to Seed for Diffusion through Social Networks|
|44||Ajitesh Srivastava, Charalampos Chelmis and Anand Panangadan. Heterogeneous Infection Rate SI model with Inter-regional Mobility|
|45||Shohei Usui and Fujio Toriumi. Statistical Analysis of Information Spreading on Arbitary Networks|
|46||Tomasz Weron and Katarzyna Sznajd-Weron. Conformity or anticonformity? – opinion dynamics in a double clique topology|
|47||Christine Marshall, Colm O’Riordan and James Cruickshank. Resistance to Defection in the Spatial Form of the Prisoner’s Dilemma Game on Random Geometric Graph Models|
|48||Bogdan Gliwa, Anna Zygmunt and Bartosz Niemczura. Comparison of nodes’ selection methods in the influence maximization problem|
|49||Jamil Civitarese, Fernanda Concatto and Cláudio Abreu. Psychological Determinants and Consequences of Temporal Networks|
Data-based study on complex systems – research on publications
|50||Tao Jia, Dashun Wang and Boleslaw Szymanski. Quantifying patterns in the evolution of scientific research interests|
|51||Vasyl Palchykov, Valerio Gemmetto, Diego Garlaschelli and Alexey Boyarsky. Discrepancies between structural network communities and external classification of physics research articles|
|52||Antonio Perianes-Rodriguez, Ludo Waltman and Nees Jan van Eck. Constructing bibliometric networks: A comparison of two approaches|
|53||Julian Sienkiewicz and Eduardo G. Altmann. Which (linguistic) factors increase the impact of scientific papers?|
|54||Seyedamir Tavakoli Taba, Liaquat Hossain, Sarah Lewis and Golnaz Alipour Esgandani. Longitudinal Collaboration Networks of Mammography Performance Research|
|55||Piotr Szymański. Predicting links between scientists in the 2nd Polish Republic based on historical co-authorship data|
|56||Krzysztof Lewiński, Adam Matusiak and Mikolaj Morzy. Data-driven Analysis of Scientific Social Network|
|57||Milos Kudelka, Jan Platos and Pavel Kromer. Author Evaluation Based on H-index and Citation Response|
Data-based study on complex systems
|58||Anna Samoilenko, Fariba Karimi, Daniel Edler, Jérôme Kunegis and Markus Strohmaier. What’s your local lingua franca? Quantifying cultural similarity through Wikipedia activity|
|59||Luca Pappalardo and Paolo Cintia. Network-based performance indicators for football teams|
|60||Forough Hassanibesheli, Leila Hedayatifar, Hadis Safdari and Gholamreza Jafari. Competition between relationships’ history and balance principle on social network dynamics|
|61||Mateusz Pomorski, Malgorzata Krawczyk, Krzysztof Kulakowski, Jaroslaw Kwapien and Marcel Ausloos. Inferring American regions from correlation networks of given baby names|
|62||Krzysztof Suchecki, Robert Paluch, Jan Choloniewski, Janusz Holyst, Flavio Fuart, Jan Rupnik, Mario Karlovcec and Marko Grobelnik. Information transfer in New York Times articles|
|63||Mateusz Wilinski. Complex correlation based synchronisation networks – clustering and causalities in the market|
|64||Irena Vodenska, Hideaki Aoyama, Yoshi Fujiwara, Hiroshi Iyetomi and Yuta Arai. Interdependencies and causalities in coupled financial networks|
|65||Daniel Morales and Guillermo Pineda-Villavicencio. Applications of Social Network Analysis to networks of medical providers|
|66||Marta Bigus and Piotr Fronczak. Mapping Polish and English WordNets Using Non-Local Topological Information|
|67||Marco De Nadai, Roberto Larcher, Nicu Sebe and Bruno Lepri. Investigating the relationships between spatial structures and urban characteristics|
|68||Tomomi Kito and Steve New. Capturing the heterogeneity and dynamics of supply relationship formations in the Japanese automobile industry|
|69||Adam Szanto-Varnagy, Peter Pollner and Illes Farkas. Mapping and navigating on the network of news events|
|70||Jerzy Surma. Social exchange in online social networks. The reciprocity phenomenon on Facebook|
|71||Jingyan Yu. Modelling the evolution of road networks|
|72||Andrzej Jarynowski, Andrzej Buda and Maciej Piasecki. Multilayer network analysis of polish Parliament 4 years before and after Smolensk crash|
|73||Mhd Wesam Al Nabki, Anna Bosch Rue and Josep Lluis de La Rosa. Topic Based Influencer Detection in Social Networks|
|74||Tymofii Brik. Studying political mobilization at the time of a conflict. A case of the “Anti-Maidan” Facebook mobilization in Ukraine.|
|75||Francesca Odella. Methodological and theoretical issues in longitudinal social networks: analysis example of multiple inter-organizational relations|
Applications of network science
|76||Gergely Tibély, David Sousa-Rodrigues, Péter Pollner and Gergely Palla. Similarity of hierarchical relationships in news portal datasets|
|77||Roman Bartusiak and Tomasz Kajdanowicz. Cooperation prediction based on Github developers network|
|78||Demet Dagdelen. Identifying spammers on Twitter|
|79||Raymond Ng. A Method for Assessing the Stability of Gene Network Modules in Complex Tissues and Subject Populations|
|80||Roman Bartusiak and Tomasz Kajdanowicz. A novel approach to words vectorization based onWordNet structure|
|81||Natalia Kudryashova. MOBILE APP ECOSYSTEM: MODELLING EFFECTS OF USER NETWORK|
|82||Antoni Sobkowicz and Wojciech Stokowiec. Steam Review Dataset – new, large scale sentiment dataset|
|83||Lukasz Augustyniak, Piotr Brodka, Tomasz Kajdanowicz and Przemysław Kazienko. Sentiment lexicon updates/extensions with word-2-vec approach|
|84||Manuel Chica Serrano, Sergio Damas Arroyo, Tomasz Kajdanowicz and Krzysztof Trawinski. Key Variable detection in System Dynamics Framework based on Multiplex|
|85||Paweł Kędzia, Marek Maziarz, Maciej Piasecki, Ewa Rudnicka and Stan Szpakowicz. PlWordNet 3.0 – a Large Lexical Network Compared with WordNet|
|86||Michał Łepek and Paweł Gąsior. The application of artificial neural networks for laser–induced breakdown spectroscopy|
|87||Antoni Sobkowicz and Paweł Sobkowicz. Automated sentiment analysis of political discussions: detection of temporal shifts in emotion expression|
|88||Gabriele Tosadori, Giovanni Scardoni, Fausto Spoto and Carlo Laudanna. Simulating Real Data Topologies with R|
|89||Adrian Popiel and Tomasz Kajdanowicz. MuNeG: Multiplex Network Generator|
|90||Michał B. Paradowski, Chih-Chun Chen and Agnieszka Cierpich. Second Language Acquisition in Study-Abroad Contexts – Insights from SNA|
|91||Ivan Srba and Maria Bielikova. Harnessing Specifics of Educational Community Question Answering|
|92||Błażej Żak, Anita Zbieg and Daniel Możdżyński. Lome.io – Online Participatory Network Mapping Platform|
|93||Thomas Hickey, Bethany Goldblum, James Kornell, Elie Katzenson and David Sweeney. A Network Model of Nuclear Proliferation|
|94||Sukrit Gupta, Rami Puzis and Konstantin Kilimnik. Comparative Network Analysis Using KronFit|
|95||Pavel Kromer, Petr Gajdos and Ivan Zelinka. Networks of Interactions and the Ant Colony Optimization Metaheuristic|
ART OF NETWORKS Exhibition: Will be presented in the AUDYTORIUM during conference
The Art of Networks was organized by Ronaldo Menezes, Associate Professor at Florida Institute of Technology and director of the BioComplex Laboratory in collaboration with Isabel Meirelles, Professor at the Faculty of Design at OCAD University in Toronto, Canada, Catherine Cramer, Stephen Uzzo and Marcia Rudy at the New York Hall of Science.
The Art of Networks would not be possible without the generosity of all authors who are participating in this special exhibition.