Trust, Recommendation Systems, and Collaboration - July 2014
Public Audience
Purpose: To highlight project progress. Information is generally at a higher level which is accessible to the interested public. All information contained in the report (regions 1-3) is a Government Deliverable/CDRL.
PI(s): John S. Baras and Jennifer Golbeck
Researchers: Peixin Gao (graduate student), Xiangyang Liu (graduate student)
HARD PROBLEM(S) ADDRESSED
This refers to Hard Problems, released November 2012.
Policy-Governed Secure Collaboration, Scalability and Composability, Understanding and Accounting for Human Behavior
A. Fundamental Research
Our goal is to develop a transormational framework for a science of trust, and its impact on local policies for collaboration, in networked multi-agent systems. The framework will take human bahavior into account from the start by treating humans as integrated components of these networks, interacting dynamically with other elements. The new analytical framework will be integrated, and validated, with empirical methods of analyzing experimental data on trust, recommendation and reputation, from several datasets available to us, in order to capture fundamental trends and patterns of human behavior, including trust and mistrust propagation, confidence in trust, phase transitions in the dynamic graph models involved in the new framework, stability or instability of collaborations.
Trust as a concept, has been developed and used in several settings and in various forms. It has been devloped and applied in social and economic networks as well as information and communication networks. An important challenge is the diversity of descriptions and uses of trust that have appeared in prior work. Another challenge is the relative scarcity of quantitative and formal methods for modeling and evaluating trust. Methods for modeling trust have varied from simple empirical models based on statistical experiments, to simple scalar weights, to more sophisticated policy-based methods. Furthermore, there are very few works attempting to link empirical data on trust (in particular data on human behavior) to various formal and quantitative models.
Our new framework is based on our recently developed foundational model for networked multi-agent systems in which we consider three interacting dynamic graphs on the same underlying set of nodes: a social/agent network, which is relational; an information network, which is also relational; and a communication network that is physical. These graphs are directed and their links and nodes are annotated with dynamically changing "weights" representing trust metrics whose formal definition and mathematical representation can take one of several options, e.g. weights can be scalars, vectors, or even policies (i.e. rules). Such models, in much simpler mathematical form, have been used in social- and economic-network studies under the name of value directed graphs. The model we are developing is far more sophisticated, and thus much more expressive. We will incorporate within such models complex human behavior in various forms.
Within this new framework that we are developing, we are specifically focusing on investigating the following fundamental problems: (a) Theories and principles governing the spreading dynamics of trust and msitrust among memebers of a network; (b) Design and analysis of recommendation systems, their dynamics and integrity; (c) Development of a framework for understanding the composition of trust across various networks at the different layers of our basic model; (d) Analysis of the effects of trust on collaboration in networked multi-agent systems, using game-theoretic and economic principles.
Various practical applications are also pursued to demonstrate the results in various practical settings.
In these investigations we principally use the following analytical methods and appropriate extensions: (i) Multiple partially ordered semirings; (ii) Constrained-coalitional games on dynamic networks; (iii) Embeddings of complex annotated graphs in nonlinear parametric spaces for the development of scalable and fast algorithms (e.g. hyperbolic networks and hyperbolic embeddings); (iv) Sophisticated statistical analysis of experimental data on trust and associated human behavioral patterns.
B. Community Interaction
J.S. Baras gave several invited, plenary and keynote lectures on the topics, approach and results in this Task:
(i) J.S. Baras, “Trust in Networks and Networked Systems,” invited distinguished lecture, TRUST Center, University of California Berkeley, Berkeley, CA, March 6, 2014.
(ii) J.S. Baras, “Hyperbolic Embedding to the Rescue in Communication and Social Networks, invited distinguished lecture, Digital Technology Center, College of Science and Engineering, University of Minnesota, Minneapolis, MN, April 23, 2014.
(iii) J.S. Baras, “Networked CPS: Some Fundamental Challenges,” invited position statement in the invited Panel on “Networking Challenges for Cyber-Physical Systems”, 2014 IEEE INFOCOM, Toronto, Canada, April 27 - May 2, 2014.
(iv) J.S. Baras, “Hyperbolic Embedding in Communication and Social Networks,” invited distinguished lecture, Computer Engineering and Telecommunications Department, University of Thessaly, Volos, Greece, May 13, 2014.
(v) J.S. Baras, “Security and Trust in a Networked Immersed World: From Components to Systems and Beyond,” invited keynote lecture, Workshop on Security and Safety: Issues, Concepts and Ideas , 2nd Hellenic Forum for Science, Innovation and Technology, Demokritos Research Center, Athens, Greece, June 30 - July 4, 2014.
C. Educational
None yet.
PUBLICATIONS
Report papers written as a results of this research. If accepted by or submitted to a journal, which journal. If presented at a conference, which conference.
[1] J.S. Baras, "A Fresh Look at Network Science: Interdependent Multigraphs Models Inspired From Statistical Physics," invited paper, Proceedings 6th International Symposium on Communications, Control and Signal Processing (ISCCSP 2014), Athens, Greece, May 21-23, 2014.
[2] X. Liu and J.S. Baras, "Using Trust in Distributed Consensus With Adversaries in Sensor and Other Networks," invited paper, Proceedings of 17th International Confernce on Information Fusion (FUSION 2014), Salamanca, Spain, July 7-10, 2014
ACCOMPLISHMENT HIGHLIGHTS