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Social Cognitive Network Academic Research Center Goals: – Leading research on social and cognitive Military Needs: – Military missions bring highly organized and disciplined soldiers to loosely organized local societies with different cultures, beliefs and value systems, with often hostile attitude to the mission goals. To achieve long term goals of the mission, soldiers need to understand dynamics and structure of social networks that they face. Our research is creating such understanding. aspects of network science – Contributing social aspect and cognitive limitations for interactions between humans and all genres of networks (communication, information and social) Center Focus: – We focus on human network interactions, – foaf:knows/foaf:based near social network, western hemisphere from the individual cognitive abilities to the group interactions in social networks that impact and are impacted by communication and information flow in groups and societies We investigate social networks ranging from formal (military, workplace), informal (social, family interactions) to hidden and adversary networks (insurgents, political 1 opponents, secret societies) Research Expertise Research Area: Accolades: – Albert Laszlo Barabasi (Northeastern U), – Institution • Rensselaer Polytechnic Institute, lead member, APS Fellow, foreign member of Hungarian • • • • • • • • focuses on network discovery and evolution, cognitive aspects of networking, valuation of – network interactions, and trust Northeastern University, member, focuses on network science and human mobility IBM Research Laboratory, member, focuses on – valuation of network interactions CUNY, member, focuses on trust and cognitive aspects of networking – MIT, focuses on social incentive in networks University of Notre Dame, focuses on trust and network dynamics and evolution – Indiana University focuses on spread processes in networks Northwestern University focuses on trust in – network interactions University of Maryland focuses on trust in – decision making Academy of Sciences and Academia Europaea, Distinguished University Professor Wayne Gray (RPI), Cognitive Science Society Fellow, Humboldt Research Award from the Alexander von Humboldt Foundation. James Hendler (RPI), IEEE Fellow, Web Expert for US Data.gov White House project, Senior Constellation Professor Alex Pentland (MIT), Design Futures Council Senior Fellow, Director of the Human Dynamics Lab Bolek Szymanski (RPI), IEEE Fellow, foreign member of Polish Academy of Sciences, Distinguished Professor, RPI Alex Vespignani (Indiana U), APS Fellow, Maria Curie Fellow, James H. Rudy Professor Al Wallace (RPI), IEEE Fellow, IEEE Third Millennium Medal, Yamada Corporation Professor 2 Research Thrusts Network Discovery (with IRC, INARC, and EDIN): – we focus on hidden networks as well as networks actively seeking camouflage, on discovery of such networks based on partial clues and on correlation between social, information and communication links in complex hidden networks Networks Robustness (with CNARC and INARC): ‒ our focus here is on controllability of networks and the relation between the structure of the network and its vulnerability to cascading failures as well as on mobility impact on caching and routing in communication networks Market-based Algorithms (with IRC and CNARC): ‒ we focus on social incentives and impact of social community on valuation of the awards, we also investigate incentive of participatory sensing, fact-discovery and reporting of findings Engineering Network Evolution (with EDIN): ‒ modeling the evolution and growth of dynamic composite networks --- from link prediction to discovery of emergent groups Cognitive Aspects of Networking (with Trust, ARL): ‒ we investigate short term (0.3-3sec) cognitive processes in humans interacting with other humans via networks and on network technology to improve human performance in network environments 3 Our research in the context of the NS CTA Roadmap Detect hidden and overlapping social networks using statistics (to ensure scalability) of observable communications and externals events Provide socially based incentives for participatory measurements and factreporting with overprovision of participants Combine organizational data, communication and information network use with inconsistent selfreported social interaction data to discover and measure workspace social networks 4 Our research in the context of the NS CTA Roadmap Use different models of opinion spreading fitted to real data to create and test predictive models of evolution of opinions in a diverse society under influence of social groups and media Using weighted network models, characterize structural properties of cascades of failures in networks of all genre under stress Classifiers/models to predict link formation and persistence, which may indicate (node) volatility and changes in network structure Develop models of human mobility based on social ties and obligations and use sophisticated techniques (entropy based, probabilistic grammars) to fit such models to real data 5 Our research in the context of the NS CTA Roadmap Use social pressure index and mobility models to optimize adhoc, social and military information gathering and routing networks to reduce bandwidth demands and enhance their performance Expand and enhance current understanding of network controllability in all genres of networks in diverse scenarios Adapt the placement and ranks of committed members of the society to enhance impact of their opinions Engineer network interfaces to match the volume and display of network information to the cognitive capabilities of humans to optimize situation awareness in combat and high stress scenarios 6 S2.1. Adversary Social Networks: Detection, Evolution, Stability and Hierarchy Key Objectives: Different colors are different types of nodes, eg.people, events, locations. Partial Cue Parent Network Identify hidden adversary networks from analyst partial cues. Grow partial cues to full communities with internal community hierarchy and information pathways. Identify influential members and stable cores. Identify community evolution and relationships between communities (e.g. opposition versus trust). Deliverables: Y2: Developed preliminary indexing schemes for social networks to find fragment queries. Developed preliminary framework for studying evolution. Developed new methods for community detection using Overlapping Communities graph embedding. Y3-Q2: Report on testing of indexing methods and evolution detection methods. Comprehensive study of community detection. Y3-Q4: Preliminary methods to handle missing links due to inability to detect full adversary networks. Key Technical Innovations Impact: • Efficient social indexing for partial adversary network Discovery of hidden adversary networks from analyst partial cues from vast noisy communications; their evolution, their interquery in social semantic databases. relationships, and stable cores. • Use statistical interaction analysis to identify hidden networks. Role Researchers • Allow networks to overlap, defining relationships Lead M. Magdon-Ismail, RPI, SCNARC between communities. Identify the hierarchies. Primary M. Goldberg, RPI, W. Wallace, RPI, N. Chawla, ND, SCNARC • Define a community as a locally optimal set. Collab. B. Uzzi, NWE (SCNARC), S. Adali, RPI (SCNARC), D. Roth, UIUC • Use streaming interactions, not network snapshots. (INARC), A. Kementsietsidis, IBM (INARC), J. Han, UIUC (INARC), X. Yan, UCSB (INARC), G. Korniss, RPI (SCNARC), B. Szymanski, • Validation on multiple scale electronic networks. RPI (SCNARC), Z. Wen, IBM (SCNARC) S2.2: Forming, Dissolving, and Influencing Communities in Social Networks Collaborations: Key Objectives: • To help counterinsurgency operations in IW, we will develop and employ individual-based models to investigate social influencing and associated strategies in social networks. Military Relevance: • “insurgency and counterinsurgency operations focusing on the control or influence of populations, not on the control of an adversary’s forces or territory” (IW, JOC, DoD, v.1.0, 09.11.07) • “Arguably, the decisive battle is for the people’s minds” (COIN FM 3-24) Impact: • Our methods and models for community detection, community stability, and social influencing will be applicable to data sets of diverse types, spanning all scales (ranging from a few hundred to millions of nodes/links), including those collected by the military. Key Technical Innovations • Develop individual-based models and methods to identify and influence communities in social networks, which manifestly emerge as the result communication and information flow across the links • Formulate mathematical foundation of connections between meta-stable configurations in social dynamics and underlying network communities • Design efficient methods and algorithms for social influencing in social networks with community structure (max-k domination, k-shell decomposition) • Dr. S. Sreenivasan (RPI) collaborates with V. Kawadia (BBN) and S. Pandit (ND) at the NS-CTA facility at BBN on LPA-based community detection in dynamic time-evolving networks • Data sharing: RPI (AddHealth high-school friendship networks ~O(100-1000) nodes); CUNY (LastFM.com online friendship network, ~2mill nodes); NEU/ND (mobile-communication graph, ~4mill nodes) • Organized Workshop at NetSci (June 7, 2011) Accomplishments: • Discovered the emergence of tipping points in social influencing • Analyzed the impact of time delays in info-social networks Road Ahead: • • • • Incentive-based influencing and spreading Competing and conflicting committed sets in social networks Efficiency of influencing with committed agents vs. mass media Combination of geographical and network effects Role Researchers Lead G. Korniss,(RPI ) Primary B. Szymanski (RPI), C. Lim (RPI), H. Makse (CUNY), Z. Toroczkai (ND), A.-L. Barabasi (NEU) Postdocs Sreenivasan, Asztalos (RPI); Gallos (CUNY); Pandit (ND); Song (NEU) Grads P. Singh, D. Hunt (RPI); W. Zhang (RPI) Collaborators V. Kawadia (BBN), B. Uzzi (NW), N. Chawla (ND), A. Pentland (MIT), M. Magdon-Ismail (RPI) Formation of Hidden Groups around Events State-of-the-Art We are only beginning to collect data on social networks that form in response to high impact events Katrina, SD 2007 Fires, Japan Tsunami Little understood about the dynamics of formation and final structure of such networks. S By tracking streaming interaction (communication) patterns, we can identify the emergence of such a hidden group. By identifying the important structural links we may either encourage or hinder the formation of the spontaneous network. Research Problem: •Understand the creation and the evolution of communities in social media in order for adversary networks to be discovered • Identify the crucial links which should either be enhanced or hindered. A spontaneous network forms around event S. The network could be adversarial. Army Need/Benefits •Study naturally occurring networks that evolve in response to events. •Facilitate discovery using large scale participatory sensing (e.g. Twitter) Such adversary networks may form in response to military events or rumors (for example a rumor that some US embassy is housing some militant). How should one deactivate such a network. Which are the important links? Wallace (RPI) Magdon-Ismail, Goldberg (RPI), ARL (SWDM 2012 at WWW 2012) 9 Formation of Hidden Groups around Events Network evolving in response to event tends to have a core plus satellite interactions that are mostly composed of dyads. The result appears to hold across different types of networks in response to different types of events. Case Studies: Empirical observations: •Very similar evolutions to similar final states for two very different case studies. •Both networks evolved to a state with a core and sparse satellite islands of communication that didn’t take-off RPI Burglar alert Courtesy of imagegossips.com Japan tsunami, 2011 Twitter re-tweet graphs Long-Term Goals Develop methods to identify the core formation early, by extending ideas from our research on identifying hidden groups in streaming data to real time monitoring settings. Develop methods to understand the important links in the formation of such networks which should be targeted either to destabilize or enhance the network. Preliminary results that indicate similarities in how network form in reaction to an event. Early identification of the core can have implications for being able to control such networks. 1 Formation of Hidden Groups around Events Behavioral Aspects Event/Warning – prescribed behavior Tweet with behavioral intent Confirmation of behavior •Event drives a warning and prescribed action •Tweets can be found around the event that exhibit the action •Behavioral onsite data that can generalize and confirm that action has been taken •Example: • Event/warning • Japan Earthquake/Tsunami – warning issued in Hawaii and evacuation was ordered • Tweet • "RT @xxxxxxxx: Prey for my brother his wife and son as they take shelter in Hawaii :(" • Confirmation of behavior • Based on the information provided by Hawaii Civil Defense there were multiple shelters opened in Hawaii and the count of the people at the shelters was obtained 11 Formation of Hidden Groups around Events Future Research Direction Develop methods to identify the core formation of a hidden group early in its evolution, by extending ideas from our research on identifying hidden groups in streaming data to real time monitoring settings. Relevance to Network Science Studying the dynamics of coordination of networks during events gives insight into the formation of ‘goal oriented’ spontaneous communities. Link formation is at the core of social network evolution, and link prediction algorithms give insight into link formation. Relevance Ability to discover hidden (especially deceptive) groups which are the most likely to be adversary is useful information to ARMY analysts. 12 Social Cognitive Network Academic Research Center Overview Boleslaw Szymanski [email protected] 11 March 2012 Highlight 1 Institutions STATUS QUO Lead PI: Alex Pentland, MIT –Social mechanisms such as taxation and subsidies do not utilize the value in social relationships –They are often both costly to implement and less effective How it works in two different contexts Crowd-Sourcing: Reward is given to referral chains as well as targets to maximize diffusion (DARPA 40th anniversary of Internet Grand Challenge) NEW INSIGHTS Carefully designed mechanisms leverage the power of social network: Peer-Pressure: –Subsidies are given to Individuals based upon –(Crowd-Sourcing) The small-world the performance of their connectivity of social network Peers. –(Peer-Pressure) Social capital in relationships can be used to significantly amplify the effect of subsidies –Thus, individuals have an incentive to exert pressure on their peers to perform well. –The peer-pressure has a stronger effect than direct subsidies to individuals based upon their performance. Social Incentives to Shape Social Networks 14 LONG TERM GOAL . QUANTITATIVE IMPACT Highlight 1 Results 8:52 Group 1: Reward individual for behavioral change; Group 2: Our idea of rewarding friends. Peer-Pressure: 2x improvement in Red Balloon Experiment: Well sustained diffusion for MIT team on the Internet, and fitness & activity levels when using the eventually winning in less than 9 hours. our mechanism as compared to traditional Pigouvian subsidies A comprehensive theory of incentive mechanism design on networks leveraging social ties for greater efficiency with applications to military teams training/combat and in the Army led projects with civilian participation Infrastructure for large-scale deployment and testing of the theory including military camps and theaters of operation 15 Highlight 2 Institutions STATUS QUO Lead PIs: Gyorgy Korniss, Bolek Szymanski, RPI Social Influence and opinion dynamics on networks: Predominantly in studies so far: • All nodes equally influencable. • No discussion of consensus engineering Main Result: Committed individuals randomly chosen from the population can guarantee a rapid change in the prevailing dominant opinion, so long as they constitute at least 10% of the population. Small committed minority is enough to make a big change. Model and Methods: • Model for social influence: Binary agreement model NEW INSIGHTS Engineering consensus on social networks: • Two competing opinions (currently prevailing vs. minority hold) Q. How do opinions change in the presence • Some fraction of nodes are committed individuals of individuals committed to an opinion? Committed individuals never change their opinion. Key new insight: A sharp transition in the time needed by committed individuals to win over the population, as their number is varied. • Simulate the model and use analytical arguments. Assumptions: All committed influencers proselytize new opinion “Never doubt that a small group of thoughtful, committed, citizens can change the world. Indeed, it is the only thing that ever has.” - Margaret Mead Highlight 2 A New (minority hold) opinion Frac. of traditional opinion holders B Old (currently prevailing) opinion For two different network types: After 10000 time steps Initially only the committed individuals hold opinion A Q. How does consensus time (time to reach all-A state), depend on percentage of committed individuals? Q. Beyond what committed fraction size is consensus engineering scalable? (i.e. time to consensus grows slowly with network size) p < pc p > pc Fraction of committed agents Critical fraction ≈ 10% To engineer consensus in feasible time, no more than 10% of committed 17 individuals are required. QUANTITATIVE IMPACT Highlight 2 Results We study the physics of how rapid consensus to a new opinion can arise We discovered a concrete lower bound in the number of “committed individuals" needed to rapidly create consensus: •Critical fraction of individuals required ≈ 10% •No knowledge of social network required: committed set can be randomly chosen. LONG TERM GOAL The path forward We aim to devise algorithms to speed up adoption of progressive and/or antiinsurgent influence through social networks given: Full knowledge of the social network Partial knowledge of the social network Constraints on number of "committed individuals“, e.g. due to a limited budget. Quantitative science of social engineering Arguably, the decisive battle is for the people’s minds” -COIN FM 3-24 18 Highlight 3 Institutions NEW INSIGHTS STATUS QUO Lead PI: Laszlo Barabasi, Northeastern U. A system is controllable if it can be driven from any initial state to any final state in finite time. A framework to study the controllability of complex selforganized systems was lacking. Combining the tools of control theory and network science opens new avenues to deepening our understanding of complex systems. Main Result: We can indentify the minimum set of driver nodes to fully control the system’s entire dynamics. How It Works: We rigorously proved the minimum inputs theorem based on an elegant mapping between structural controllability and maximum matching. Assumptions and Limitations: We assume that link weights are either fixed zero or independent free parameters. We discovered a novel theorem that enables identifying the minimum set of nodes through which the control input can steer the network to the desired future state. 19 Highlight 3 Application: How to find the minimum set of nodes to control the whole system? Complement of a maximum matching (a set of edges without common heads or tails) is the minimum set of nodes through which network can be controlled. LONG TERM GOAL QUANTITATIVE IMPACT Highlight 3 Results We address controllability for arbitrary network topologies and sizes and find that: 1. The minimum set of driver nodes can be exactly identified, with ND = max{1, Nunmatched}. 2. ND is mainly determined by the network degree distribution. 3. Driver nodes tend to avoid hubs. Effects of higher order correlations (degree assortativity, clustering coefficients, modularity, hierarchy structure) on network controllability. Robustness of network controllability against both random failure and intended attack. Optimal design of network structure considering both controllability and robustness. To appear on May 12, 2011 21 Closing Remarks Social and cognitive aspect of networking are an important component in understanding all genres of networks and discovery of laws of network science Key Publications: ( all inter-center) Le, H., Pasternack, J., Ahmadi, H., Gupta, M., Abdelzaher, T., Han, J., Roth, D., Szymanski, B., Adali, S. 2011. Apollo: – Market-like Processes in Networks: SCNARC(MIT, Towards Fact-finding in Participatory Sensing. ACM/IEEE RPI), IRC(Harvard, BBN, U Michigan), CNARC(PSU) IPSN (Demo Paper)Reference (INARC, SCNARC) – Srivastava, M., Abdelzaher, T., Szymanski, B. 2011. Human – Network Discovery: SCNARC(RPI, ND), IRC(BBN), Centric Sensing. Philosophical Transactions of the Royal INARC(UIUC, CMU) Society (INARC, SCNARC) – Tong, H., Prakash, B., Tsourakakis, C., Eliassi-Rad, T., – Cognitive Aspects of Networking: SCNARC(RPI, Faloutsos, C., Chau, D. 2010. On the Vulnerability of Large CUNY), INARC(PARC), ARL Graphs. ICDM 2010 (IRC, SCNARC) – Valuation of Network Interactions: SCNARC(IBM, – Valler, N., Prakash, B., Tong, H., Faloutsos, M., Faloutsos, C. 2011. Epidemic Spread in Mobile Ad Hoc Networks RPI, ND, NEU), INARC(UIUC), CNARC(PSU) Determining the Tipping Point. IFIP Networking 2011 (IRC, SCNARC) Metrics (for FY11 only): – Zhuo, X., Cao, G., Szymanski, B., La Porta, T. 2011. SocialBased Cooperative Caching in DTNs: A Contact Duration – Papers: Journal/Chapter (17), Conference (22) Aware Approach. SECON 2011 (CNARC, SCNARC) – Ph.D. Students On Board (21) Postdocs (7) – Pandit, S., Yang, Y., Kawadia, V., Chawla, N., Sreenivasan, – Ph.D. Students Graduated (2) S. 2011. Detecting Communities in Time-evolving Proximity Networks. IEEE Network Science Workshop (IRC, SCNARC) – Yang, Y., Sun, Y., Pandit, S., Chawla, N.V, Han, J. 2011. Is Objective Function the Silver Bullet? A Case Study of Transitions: Community Detection Algorithms on Social Networks, – In planning ASONAM, 2011. (INARC, SCNARC) Key Collaborations Enabled: – 22 S1.2: Social Incentives to Shape Network Actions and Responses Efficient Incentives for Network with “Selfish” Agents Y2 Accomplishments: An optimal recruitment mechanism for DARPA challenge campaign A new, efficient incentive mechanisms for network exchanges Congestion control auction for bursty network traffic from tracking mobile targets Deliverables: Q2. Formal analysis and experiments on role of peer-pressure on network performance; auction parameter sensitivity for participant selection Q4. Impact of balanced referral chain and provider awards on campaign performance; benefits of balanced peer-pressure and individual awards for network performance Key Technical Innovations - Design of participation preserving incentive compatible auction mechanisms for dynamic and opportunistic selection of the agents and/or sensors for data collection - Study of incentive systems for recruitment and sustainable participation in crowd-sourcing campaigns - Research on reward systems for peers in behavior modification in social networks Key Objectives: - Efficient and sustainable incentives for recruitment and sustainable participation of network members in crowd-sourcing campaigns - An auction mechanism for pricing participatory sensing by devices embedded in the cell phones of the civilian population - Peer-based reward system for behavior modification in social networks Impact: An integration of participatory sensing/monitoring and routing to enable better use of resources and unobtrusive and difficult to detect civilian participation in the anti-insurgent campaigns. Novel incentive and reward mechanisms for participation and network exchanges with “selfish” (non-collaborative) nodes . Role Researchers Lead B.K. Szymanski, RPI, SCNARC Primary A. Pentland, MIT, SCNARC Collab. M. Wellman, U. Michigan. IRC, V. Kawadia, BBN, IRC, A. Goel, Stanford. CNARC. T. Abdelzaher, UIUC, INARC. S1.3: Socially-Driven Models of Human Mobility and Its Use in Delay Tolerant Routing Less noise but fewer key fact More noise and key facts Less noise but fewer key fact Key Objectives: Methods to capture the sophisticated relations between nodes that define human mobility patterns. Understanding the interplay between physical space and temporal interactions on social networks Deliverables: *Significant difference throughout Less noise but fewer key fact Exposure to key facts and “noise” in hierarchy Last Year Accomplishments - Analysis of network dynamics and spreading processes and understanding how does the timing of relationships affect spreading Q1: Visualization for small scale episodic network data Q2: Usage of PCFGs for human mobility modeling in different scenarios Q3: Measurement of impact of structural features of temporal proximity on spreading/routing Q4: Evaluation of efficiency improvement from human mobility modeling in participatory data collection, sensing and routing - Defined Social Pressure Metrics (SPM) for Routing and Impact: Caching to represents human mobility - Ability to analyze the impact of mobility on network structure - Investigated participatory sensing and fact-finding Key Technical Innovations - Methodology to use Probabilistic Context Free Grammars for strong predictive models for capturing human mobility. Analyzing finer granularity of information on physical proximity to understand spreading processes where dyadic probability of diffusion is very high. Investigating methods for identifying the distinct archetypes that characterize communication patterns Role Researchers Lead D. Lazer, NEU & Harvard, SCNARC Primary B. Szymanski, RPI, SCNARC Collab. T. La Porta, PSU, CNARC, G. Cao, PSU, CNARC, T. F. Abdelzaher, UIUC, INARC, M. Srivatsa, IBM, INARC, A. Pentland, MIT, SCNARC S1.4: Composite Networks in Organization and Team Performance Key Objectives: - (T1) Characterization. Understand how the structure of composite networks in organization affects the team performance. - (T2) Prediction. Investigate what kinds of network metrics are crucial to team performance and how to predict the performance of a given team before they actually work on the assigned task. - (T3) Formation. Automatic team forming recommendations to optimize the team performance based on network metrics Individual Performance (measured on the individual revenue) after using organizational social network analysis tool (SmallBlue). We observed an 11% increase of performance enhancement. We shall further discover network factors for team performance.. Key Technical Innovations - Novel causality understanding on how networks impact team performance, through objective observation and measurements. - Novel Predictive models to forecast the performance of a given team based on its collective network structure.. - Novel algorithms to automatically find a good team of experts to perform a particular task, by optimizing the expertise composition, combined social networks, and historical and predictive network evolution. \ Deliverables: Q1: Investigating team performance characterization. Q2: Design algorithms for team formation. Q3: Investigating dynamic aspect of performance. Q4: Implement and test the algorithm for team formation. Impact: - Ability to utilize networks to improve team performance and team formation. Role Researchers Lead H. Tong, IBM, SCNARC Primary K. Ehrlich, W. Zhen, C.-Y. Lin, IBM, S. Wasserman, Indiana U, R. Cross, UVA, SCNARC Collab. L. Wu, U Penn, SCNARC, X. Fan, UCSB, INARC, D. Wang, NEU, SCNARC S2.3: Controllability of Complex Networks Finding Control Nodes in the Network Deliverables: Q1. Analytical results on calculating single node controllability and the role of controllability in information propagation Q2. The role of network evolution on single node controllability and spreading and control in modular networks Q3. Validation of the developed approach on real network data Q4. Validating single node controllability, summary of the impact of network evolution on controllability and the relation between nodes controllability and nodes relevance in information diffusion processes Impact: Single node controllability will offer tools to establish the influence of individual nodes Novel algorithms will enable better control of the spread of ideas and pathogens Key Technical Innovations - Combine classical engineering control theory with network science Understanding controllability of self-organizing networks and system will improve our ability to “influence, adapt, and approaches to dynamic processes on networks to develop optimize the behaviors of composite networks to support a fundamental understanding of network controllability mission's success” - Study the impact of the network structure in which a node is Role Researchers embedded on this node ability to control the network dynamics - Combine formal analysis, predictive numerical and computational Lead A.-L. Barabasi, NEU, SCNARC model and empirical data for all genres of networks from J. George, Civ, ARL/SEDD, J.-J. Slotine, MIT, communication to information to biological and to social systems in Collab. L. Adamic, U Michigan, INARC analyzing network controllability. Key Objectives: - Mathematical underpinnings of single-node controllability - Controllability and communication/information spreading processes - Evolution of controllability in self-organizing networks