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Complex Networks SNA 2013
Complex Networks: May 21-26 2013, Doug White, Tolga Oztan, Robert Sinkovitz, Telmo Menezes. Paper title: UC Complex Social Science (CoSSci) Gateway (under development): Autocorrelation Modeling, Kinship Network Modeling, k- and pairwise cohesion in Large Networks & Open Opportunities for Online Education. XXXIII XXXIII Sunbelt Social Networks Conference of the International Network for Social Network Analysis (INSNA). Hamburg, Germany. You can experiment with the biology prototype of our Galaxy Social Science Gateway, and practice with their public server.
- Abstract: The UC Complex Social Science (CoSSci) Gateway (portal implementation 2003 at UCI/SDSC@UCSD) provides remote access for researchers and classrooms or online classes to do advanced computing. (Large) network k-cohesion (White et_al.) and pairwise cohesion (Oztan et_al.) return linked lists of all k-connected subsets and k-connected pairs. Menezes´ Synthetic tools analyze and perform evolutionary modeling of complex networks, including the 90+ kinship networks in *net format hosted at the Kinsources website, and return variables for societal databases such as those below.
- Graphs of potential Causal effects can be modeled for rectangular databases with network W matrices for inclusion of autocorrelation effects. Data and modeling tools are available on-line at (CoSSci) for a growing number of datasets. Currently these include the Ethnographic Atlas (n=1500 societies), Standard Cross-Cultural Sample (n=186), Binford´s Foragers (n=339), Jorgensen´s Western Indians (n=172), and will eventually include many new cross-national, cross-polity, cross-corporate and comparative psychology datasets. Each new dataset requires its own W-matrix networks, and if missing data are to be imputed, with principal components of fully coded data suitable for multiple imputation. These datasets are intended for use in online courses (Coursera; Moodle) on Complex Networks, Cross-Cultural/-Polity/-National/-Economic studies, quantitative methods in the Social Sciences, and a great variety of topical courses. Results of early studies are reported. A Wiley 2013 textbook, Companion to Cross-Cultural Research (Eds. White, Eff, Dow, Gray), will be useful for instructors and contains chapters published on-line that are useful guides for students learning complex network and comparative approaches in the Social Sciences. Principal keywords: Causality, Complexity.
- Keywords: Software, Statistics, Community, Culture, Kinship. Principal keyword: Causality.
- Session choice? Mathematical and Statistical Network Models?
- Networked communities: Ethnographies on social relations, mobilities, and belonging. A panel in honor of Waltraud Kokot [Michael Schnegg]
- The appropriate session name for this paper is Complex networks
- Organizers or Session leaders may find this paper to fit in some other session. Relevant topics include Kinship networks* Online courses* (these are not in the list)
- Cross-national/Cultural comparison
- Large Scale Networks Analysis
- Mathematical and Statistical Network Models
- Networks and Society
- Network Evolution
University of California Multi-Campus Complexity Events 2011-2012
Doug White. Theory of Forager Networks: Simulation, Comparative Data, and Evolution of jHuman Cohesion and Cooperation IMBS Colloquium October 11 2012. Summary.IMBStalk.pdf. YouTube: Network Theory for Evolution of Cooperation Among Foragers
Doug White and Tolga Oztan. Nov. 16, 2012. Foragers, Kinship Networks, and the Evolution of Cooperation. Session on Boundaries of Discipline, Boundaries of Kinship organized by Dwight Read. American Anthropological Association Annual Meeting, San Francisco, CA. Revised version Nov. 18, 2012.
Fall Quarter 2011
Complex Network Seminar Thursday, October 27, 2011 David Rideout DANCES
UCSD Complex Network Seminar DANCES- Different Angles on Network Complexity, Engineering, and Science (DANCES) senior talk by David Rideout, department of Mathematics, UCSD.
David will speak about Causal Networks as one of possible approaches to Quantum Gravity.
Talk Title: Quantum gravity as a causal network Abstract: The attempt to construct a theory of gravity which is consistent with quantum physics has led physicists to believe that space and time itself possesses a sort of atomic structure at very small length scales, analogous to the atoms of ordinary matter. Such a structure can be modeled by a collection of 'spacetime atoms' which are connected by causal links. I will describe gravity in terms of such a causal network, called a Causal Set, and show how one can construct a dynamical law for the network's growth in terms of two fundamental physical principles. The resulting dynamical law produces causal networks with an intriguing similarity to the universe in which we live.
Date: Thursday, October 27, 2011. Time: 12.00 p.m. (pizza and refreshments are served at 11.45 a.m.) Place: San Diego Supercomputer center, Room 408 (Central).
The goal of the seminar is to bring together junior and senior researchers, including UCSD graduate students and post-docs, studying networks. The seminar will foster communication and collaboration among researchers from diverse disciplines that study networks from different perspectives (physics, biology, sociology, computer science, ECE, math, bioengineering, cognitive science, etc), and provide young researchers a forum to practice their presentation and communication skills.
Seminar Format The two-hour seminar meets every second thursday of the Fall 2011 quarter at 12p.m. Each meeting consists of one or two 45-minute talks, either on one's own or related to one's own research, designed for general audiences. Informal conversation and refreshments will precede the seminar. Participants are expected to present approximately once per (2) quarter(s).
Organizer / Contact If you are interested in this seminar or have some questions or feedback, please contact Maksim Kitsak <mkitsak at caida dot org>.
Location Unless otherwise noted, seminar meeting takes place at the San Diego Supercomputer Center (SDSC), room 408 (West wing). See map of SDSC's location on UCSD campus.
Complex Network Seminar Noon Monday, 14 November, 2011 Massimo Franceschetti
Prof. Massimo Franceschetti, Assoc Professor, Electrical & Computer Engineering, UCSD, who will speak about Human matching behavior in social networks
IMPORTANT: Note the unusual date for the seminar: MONDAY, November, 14, 2011. Time: 12.00 p.m. (pizza and refreshments are served at 11.45 a.m.) Place: San Diego Supercomputer center, Room 408 (Central).
Talk Title: Human matching behavior in social networks: an algorithmic perspective
Abstract: In this talk we argue for an algorithmic approach to understanding the collective dynamics of human behavior. We consider the distributed game of pairing up individuals connected over a network of social contacts. Our experimental set-up is simple. Individuals are represented by nodes of a network with edges representing potential matches. They are connected over a virtual network and interact with their neighbors through a computer interface. They are given only local information about the network, and can only communicate with their immediate neighbors. They have the shared goal of maximizing the total number of matches in the network. We have conducted over 200 experiments with human subjects on a pool of over 50 networks with up to 24 nodes each. From a first set of experiments we identify a behavioral principle called prudence and develop an algorithmic model to analyze its properties mathematically and by simulations, and finally validate the model with additional human subject experiments for various network sizes and topologies. We show that the human subjects largely abide by prudence and their collective behavior is closely tracked by the predictions of the mathematical model. Hence, we argue that 1) observational data collected from experiments on human subjects interacting using a simple computer interface can be useful to uncover basic behavioral properties such as prudence, that may not be apparent from the more classic approach of off-line surveys; and 2) algorithmic modeling and the mathematical analysis of algorithms can be a useful tool to systematically predict aggregate social behavior and the dynamics of coordination over social networks. Possible extensions of this work to hybrid computer-human networks, preferential goals, and incentive mechanisms will be discussed.
Complex Network Seminar Noon Monday, 17 November, 2011 Daniel Ricketts
I would like to announce the next meeting of the UCSD Complex Network Seminar (DANCES). Our speaker this Thursday is Daniel Ricketts who will speak about the impact of historical information (memory) in human coordination.
Date: Thursday, November, 17, 2011. Time: 12.00 p.m. (pizza and refreshments are served at 11.45 a.m.) Place: San Diego Supercomputer center, Room 408 (Central).
Talk Title: Impact of historical information in human coordination
Abstract: Planning, scheduling, and resource allocation are examples of real-world situations in which humans must solve complex combinatorial problems. It is often the case that no global entity is present, and a network of inter-personal influences is the conduit for the solution. In these cases, humans must solve combinatorial problems in a distributed fashion. We call these coordination problems. We know from computer science research that distributed algorithms sometimes use considerable information about the past to reduce the time to coordination. Furthermore, we know that a history of inter-personal interactions can create strong behavioral biases in humans, potentially influencing their behavior. We want to know whether humans use this historical information in solving coordination problems, and if they do so to their advantage. Using graph 2-coloring as a simple representative of coordination problems, we perform human subject experiments and agent-based simulations designed to elucidate the role of historical information in human coordination tasks. We report on the game dynamics and average completion times for these experiments.
Hope to see you al at the seminar!
Best regards, Maksim Kitsak
P.S. More info is available at http://www.caida.org/workshops/dances/
Thanks for the sign test suggestion. I'll take a look at what we can learn from that.
Michael Kearns has a number of papers in this area:
http://www.cis.upenn.edu/~mkearns/papers/ScienceFinal.pdf http://www.cis.upenn.edu/~mkearns/papers/ColConPNAS.pdf http://www.cis.upenn.edu/~mkearns/papers/behvoting.pdf http://www.cis.upenn.edu/~mkearns/papers/kingpawn.pdf
The first two papers are on graph coloring and consensus. The third paper is also on graph consensus but introduces a slight variant in which some subjects are paid more if a particular consensus outcome is reached. The fourth paper is on a different network game. Also, in case you're interested in reading more about what I talked about today, here's an extended abstract on what we did:
Complex Network Seminar Thursday, Noon 1 December, 2011 Charles Elkan
I would like to announce the next meeting of the UCSD Complex Network Seminar (DANCES). Our speaker this Thursday is Professor Charles Elkan, Computer Science & Engineering, UCSD.
Date: Thursday, December, 1, 2011. Time: 12.00 p.m. (pizza and refreshments are served at 11.45 a.m.) Place: San Diego Supercomputer center, Room 408 (Central).
Talk Title: Learning to make predictions in networks
Abstract: Many phenomena in social science and in biology are represented naturally as graphs or networks. Typically, the relevant networks are only known incompletely, so a fundamental research question is how to infer missing information in a network, given all available known information. In this talk I will present a general method for learning from known nodes and edges in a graph to predict labels for other nodes and edges. The new method induces latent features to represent implicit properties of nodes, and combines the latent features with any available explicit features to make accurate predictions. The method is an extension and combination of matrix factorization and a log-linear model. For link prediction, the new method achieves state of the art accuracy over a wider range of datasets, from biology and from social science, than any previous method. (Joint work with Aditya Menon.)
Hope to see you all at the seminar. More info is available at: http://www.caida.org/workshops/dances/
Best regards, Maksim Kitsak
Winter-Spring Quarters 2012
Elkan Seminar February 5, 2012 Doug White
UCSD AI seminar, organized by Charles Elkan. All talks are at 12:15pm on a Monday, in room 4140, in the CSE building on the UCSD campus.
Cohesive Subnetwork Causality in the Evolution of Cooperation
- How did humans come to be nongenetically prosocial?
- (from foragers to producers)
Revised abstract: Network measures of structurally cohesive groups and pairs of individuals within and linked to (stru-)cohesive groups have a powerful record of causal predictions in social networks generally. Here these concepts are applied to forager societies studied ethnographically and coded for ethnographic variables in Binford's "Frames of Reference" and the "Kinsources" network datasets, each of which contain numerous measures of social cooperation. Hypotheses are presented and tested for the extent to which network and stru-cohesion measures outperform existing rules for the evolution of cooperation in human groups.
- Comment by Dwight: I assume you mean by "existing rules for evolution of cooperation" something like the work done on game theory approaches, especially prisoner's dilemma, to the evolution of cooperation? That is, you are countering those who look at evolution of cooperation from an individual trait model of evolution, whether transmission is genotypic or phenotypic? If so, I quite agree with you.
- It sounds like you and I are reaching very similar conclusions, but from different routes.
- Network measures of structurally cohesive groups and pairs of individuals within and linked to (stru-)cohesive groups have a powerful record of causal predictions in social networks generally.What kinds of predictions? Here these concepts are applied to forager societies studied ethnographically and coded for ethnographic variables in Binford's "Frames of Reference" and the "Kinsources" network datasets, each of which contain numerous measures of social cooperation. Hypotheses are presented what hypotheses? and tested for the extent to which network and stru-cohesion measures outperform existing rules what rules ? or kinds of rules? for the evolution of cooperation in human groups. Any conclusions reached regarding what this implies about genetic models of cooperation?
Network measures of structurally cohesive groups and pairs of individuals within and linked to (stru-)cohesive groups have a powerful record of causal predictions in social networks generally: e.g., studies of attachment to community, school, and cohesive sets of corporate partnerships/political attachments. Here these concepts are applied to forager societies studied ethnographically and coded for ethnographic variables in Binford's "Frames of Reference" and the "Kinsources" network datasets, each of which contain numerous measures of social cooperation. A main hypothesis tested is that network and stru-cohesion measures outperform existing rules kin selection, genetic and game theoretical approaches to the evolution of cooperation in human groups. I show how group attachment cohesion with benefits of cooperation need not depend on genetic models of cooperation or group selection. Rather the pan-human biparental and role-based structure of human kinship, in all of its variants, provides low-order or egalitarian cohesive structure. Hierarchical cohesive structure (of order 5 and higher), however, has been shown to lead to bullying and the emergence of selfishness as network-dependent phenomena and a novel conception of human evolution.
- Have you seen the article below? (...Marlowe, ...Fowler et al 2012 Science). The authors conclude "cooperative behaviour may be best understood as a process influenced by a combination of not only genes and environment, but also social networks", which shows they are headed in the right direction, but they seem to assume that networks, hence the structure of networks, arise essentially out of marriage and friendship choices. Kinship for them is biological kinship, hence the whole cultural dimension of cultural kinship (as opposed to biological kinship) and its implications for social structure (hence an important aspect of social networks) is absent from their perspective.
Earlier abstract: The 1927 Menger theorem proves the equivalence of a maximal k-cohesive set of nodes in a network to a maximal set of nodes in which all pairs of nodes are at least k-connected. We call these equivalent concepts stru-cohesion. It has a record of powerful causal predictions in social networks. This talk will present six types of examples, and end with a discussion of how stru-cohesion outperforms existing rules for the evolution of cooperation in human groups.
Old Abstract: I examine structural cohesion as an explanation of the evolution of human cooperation, competing with the five genetic “rules” for evolution of cooperation (Nowak). An anomaly is the appearance of egalitarian reverse dominance among foragers, who punish bullies.
- Structural and pairwise cohesion (Menger 1927) are newly rediscovered theoretical and empirical measures for the social and informatic sciences (2001, 2001, 2003) with strong predictive properties borne out along with new discoveries in the last decade in many different domains. A newly discovered feature of str-cohesion is the egalitarian/hierarchical distinction, reported in studies of 42 American classrooms, where bullying behavior is found only with hierarchical cohesion.
- Egalitarian str-cohesion in human kinship appears with the advent of biparental networks (unlike catarrhines generally, where one parent, the male ape or female OW monkey, dominates a ranked hierarchy). Comparing cohesion structures to models for emergence of genetic altruism, the evidence favors stronger causality for str-cohesion and its variants. The conclusion proposed is that biparental kinship networks have low str-cohesion favoring survival among foragers through social evolution of cooperation rather than genetic and game-theoretic mechanisms (or group selection). Once non-kinship relations cross-cut kinship, as in chiefdoms and states, hierarchical cohesion reappears but causes outcomes that differ according to context and function of different ties.
- As opposed to the "selfish" gene models, its nobody's genes in particular that are necessarily passed along by "reproductive success" in str-cohesive groups. Cooperative groups may be favored but not necessarily through group genetic selection since learning to be cooperative in groups can be the mechanism of replication and individuals can enter and leave cooperative groups. Nor do benefits necessarily accrue equality to members of cooperative groups.
- Its worth investing in algorithms for str-cohesion and pair-cohesion-- even though they are 'hard' --because these concepts measure causal forces in social networks and may lead to radically different understandings of social dynamics, not all based on axioms of selfishness that may lead to enormous biases in simulations when compared to observable data.
In any case, of the 5 rules for the evolution of cooperation, only one is directly genetic, with very limited effect. The rest are social mechanisms. Of these some research areas show that cycles are effective in that costs of links are minimized, and it other research areas it is a threshold of convergent redundancy of links that is effective, as in str-cohesion.
- Apicella, Marlowe, Fowler & Christakis. 2012. Nature 497 Social networks and cooperation in hunter-gatherers. http://www.nature.com/nature/journal/v481/n7382/full/nature10736.html
- K.Menger 1927. http://en.wikipedia.org/wiki/Menger's_theorem
- M.Nowak 2006. Five Rules for the Evolution of Cooperation. Science 314 http://hvrd.me/yBUDV2
- D.White and Frank Harary 2001. The Cohesiveness of Blocks in Social Networks: Node Connectivity and Conditional Density. Sociological Methodology 2001 (31):305-359.
- D.White and Mark Newman 2001. Fast approximation algorithms for finding node-independent paths in networks. Santa Fe Institute working papers 01-07-035.
Complex Networks Seminar (SDSC: DANCES) Thursday June 6, 2012 Doug White and Tolga Oztan
- later comment post hoc: Having opened question of types of cohesion, political bullying by the 1% is seen in various hierarchical cohesion network structures: the Reptile versus Dem network of cosponsorships, who reads what books etc (does this last figure have a statistical difference between the left net and the right net in terms of hierarchical versus more egalitarian cohesion) . There is also Reverse Bullying where the cohesion is dense but not hierarchical, as in the network of petition responses to the murder of Travor Martin.
Effects of Structural Cohesion in Forager Networks in the Evolution of Cooperation
Network measures of structurally cohesive groups and pairs of individuals within and linked to (stru-)cohesive groups have a powerful record of causal predictions in social networks generally: e.g., studies of attachment to community, school, and cohesive sets of corporate partnerships/political attachments. Here these concepts are applied to forager societies studied ethnographically and coded for ethnographic variables in Binford's "Frames of Reference" and the "Kinsources" network datasets, each of which contain numerous measures of social cooperation. A main hypothesis tested is that network and stru-cohesion measures, now available in R (igraph), outperform existing rules of kin selection, genetic, and game theoretical approaches to the evolution of cooperation in human groups. Instead, cooperation in networks is produced by an interaction between culture and sexual selection.... Group attachment cohesion with benefits of cooperation need not depend on genetic models of cooperation or group selection. Rather the pan-human biparental and role-based structure of human kinship, in all of its variants, provides low-order or egalitarian cohesive structure. This leads to a novel conception of human evolution, contra Richard Dawkins. Hierarchical cohesive structure (of order 5 and higher), however, has been shown to lead to bullying and the nongenetic emergence of selfishness as network-dependent phenomena.
- Comment by Dwight Read: Definitely compatible (with his approach and Murray Leaf's) and adds another aspect (to the AAA Dec session in San Francisco), namely not only does cultural kinship have associated with it beliefs about appropriate kinship behavior (Fortes's Axiom of Amity) through which sharing and cooperation are reinforced and thus cultural systems transcend individual "psychology" or "traits" through rule-defined behavior, but (as I read what you are saying), what is at the conceptual/ideational level translates into behavior leading to networks with structure that reinforces sharing and cooperation.
Test-case sample for Tolga: For further testing in year.2 of kinship network hypotheses, Boehm’s new codes for a set of 53 LPA societies will cover 11 forager cases in our Kinsources database that have genealogical networks for the cohesion analysis and GSR Oztan's thesis: eight Inuit (No. Alaska, NW Alaska, Baffinland, Copper, Iglulik, Labrador, Netsilik, Nunamiut); three others (Kung, Yolngu Murngin, Vedda); three more that could be coded from community-wide genealogies (Agta, Tiwi), and possibly others from the eHRAF files; thus cases for seven or more of the eleven forager regions. The evolutionary hypotheses we want to test are that: 1) those subnetworks with more structurally cohesive ancestral groupings have significantly more offspring on average than others, and 2) those networks with the more cohesive cores have more of Boehm’s indicators of prosociality. We should be able us to start on these questions earlier rather than later in the project by placing these 11 societies at the top of the queue for new coding by Boehm’s coder. There are 29 forager societies with no animal or cultigen dependence in the SCCS, including five in Kinsources (Agta, Apache, Copper Eskimo, Kung, Netsilik), and seven in Boehm’s forager sample (Copper Eskimo, Eyak, Kung, Mbuti, Semang, Vedda, and Yurok). Type of supernatural sanctions for these societies and for the seven SCCS fully forager societies will also be coded early by Boehm’s research coder. She will be doing further coding of supernatural sanctioning but not of social sanctioning.
Dwight: Did I ever mention to you the book I published last fall called "How Culture Makes Us Human: Primate Social Evolution and the Formation of Human Societies" (published by Left Coast Press)? Whereas the book Murray and I co-authored picks up the story about the time of the upper paleolithic and argues for the foundation of human societies via events going back to that time period, this book picks up the story with the Old World monkeys and the chimpanzees and provides the evolutionary foundation for the Murray and Read book. (What the book covers won't be unfamiliar to you as I gave a talk on this topic in our 4-campus symposium.) I argue that the trend from the OW monkeys to the Chimpanzees leads to a "deadend" (in the sense of reaching a point where further evolution via biological processes alone was no longer feasible), not to human societies as they actually developed through hominin evolution. I make extensive use of the increased individuality of primates as making for (potentially) vastly more complex social environments, leading to chimpanzees with high individuality reverting to smaller social units (solitary females, males with groups of 6 on the average) that are not cohesive -- male groups fission on a few hours to a day time scale. This is a trend that does not carry one forward from a chimpanzee-like form of social organization to what happens with the hominins and the development of human forms of social organization. The key change, I argue, is the change from face-to-face interaction (non-human primates) to relation based forms of social organization -- this could not occur with chimpanzees because of their limited size of working memory. 'I argue that a key property that emerges from forming new relations from relations of relations is that reciprocal social behaviors (such sharing, cooperation) will now arise when all the participants share the same system of relations and associated behaviors. (The reciprocity of kinship relations becomes the framework for the reciprocity of social relations.) Thus the boundary of the social field becomes a cultural kinship boundary out of necessity for a relational system of social organization to "work." The cultural systems of kinship that develops makes possible cohesive social units that can incorporate not only the members of one's own group, but other groups with which there are cultural kin ties.
So, like your argument, this counters the "cultural evolution" arguments as none of them account for the shift from face-to-face social interaction to relation based social systems as this is no longer evolution via individual traits, but involves evolution in the forms of social organization in which individuals are embedded. Like you, I see that social cohesion is a key aspect of what is going on, though I'm only addressing it from a conceptual framework, not from the a network structure viewpoint. The two are, of course, complementary viewpoints.
HSC Past talks
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- The Human Sciences and Complexity Seminars began in 2005-2006 on a biweekly basis through intercampus interactive video-meetings. They are in their fourth year. The seminar wiki, http://intersci.ss.uci.edu/wiki, has a diverse and multidisciplinary speaker list, with emphases on empirical, simulation, network-dynamical, and mathematical modeling of complex processes in the full range of human sciences. UC Davis CSC joins UCSD, UCI and UCLA in 2008-2009 along with participants from complexity research centers.
Calit2 UCI and UCSD
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Elsewhere: Complexity Events
UCI RIGS: Research in International Global Studies with pdfs for papers
- Dirk Helbing’s inaugural lecture
- ETH Zurich International Workshops on Challenges and Visions in the Social Sciences August 18-23, 2008
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