SNA Notes

CS6010 Social Network Analysis Notes by lecturer

UNIT IV   PREDICTING HUMAN BEHAVIOUR AND PRIVACY ISSUES    

Understanding and predicting human behaviour for social communities - User data management - Inference and Distribution - Enabling new human experiences - Reality mining - Context - Awareness - Privacy in online social networks - Trust in online environment - Trust models based on subjective logic - Trust network analysis - Trust transitivity analysis - Combining trust and reputation - Trust derivation based on trust comparisons - Attack spectrum and countermeasures.

Enabling New Human Experiences

It is important to understand what are the technologies behind user data management, how to link them and what can they achieve when combined in synergy.

1. The Technologies

a. Social Networks

• Humans in all cultures at all times form complex social networks
• Social network - means ongoing relations among people that matter to those engaged in the group, either for specific reasons or for more general expressions of mutual agreement.
• Social networks among individuals who may not be related can be validated and maintained by agreement on objectives, social values, or even by choice of entertainment.
• involve reciprocal responsibilities and roles that may be selfless or self-interest based.
• Social networks are trusted because of shared experiences and the perception of shared values and shared needs
• Behavior of individuals in online networks can be slightly different from the same individuals interacting in a more traditional social network (reality).
• It gives us invaluable approaches on the people we are communicating with, which groups are we engaged, which are our preferences, etc.

b. Reality Mining

• To overcome the differences between online and “offline” networks, reality mining techniques can be empowered to approximate both worlds, proving awareness about people actual behavior.
• It is the collection and analysis of machine sensed environmental data pertaining to human social behavior.
• It typically analyzes sensor data  from mobiles, video cameras, satellites, etc
• Predictive patterns such as ‘honest signals’ provide major factors in human decision making.
• Reality mining enables ‘big picture” of specific social contexts by aggregating and averaging the collected data
• It allows data/events correlation and consequently future occurrences extrapolation.

c. Context-Awareness

By assessing and analyzing visions and predictions on computing, devices, infrastructures and human interaction, it becomes clear that:

a. context is available, meaningful, and carries rich information in such environments,

b. that users’ expectations and user experience is directly related to context,

c. acquiring, representing, providing, and using context becomes a crucial enabling technology for the vision of disappearing computers in everyday environments.

2.  Architectural Framework and Methodology

To enable human behavior understanding and prediction, there are several independent but complementary steps that can be grouped into three different categories:

ü  Data Management

ü  New Knowledge Generation and

ü  Service Exposure and Control.

a. Data management

• This activity usually starts with data acquisition.
• Involves gathering of information from different information systems.
• Figure depicts these relationships as well as the sequence of activities involved.

• Data is not usually captured without errors. Therefore it is necessary to preprocess it in advance before mining.
• Otherwise it would not be possible to correlate information correctly.
• Once this is done, data is mined by using two different approaches:

ü  know statistical algorithms to help pattern recognition and consequent algorithmic modeling,

ü  the opposite approach, where specific algorithms are designed to identify patterns in the data (this requires previous modeling).

Combining both, allows us to address the specifics of our applications, and at the same time, automatically detect new relevant correlations that might occur after a few iterations.

b. Knowledge Generation

• New information inference is based on user related data (called as context)
• Three different categories:
• Real-time
• Historical data
• Reasoned context
• In Fig. below, there are several layers of abstraction in a context-aware system and any context-aware middleware or architecture must therefore be capable of building representations and models of these abstractions.
• However, these high-level abstractions can only be made from lower level context, which requires some form of context management function (performed by a Context Broker).
• In our case, this is performed at the Human Data Repository.
• The main context management features are context acquisition, context aggregation & fusion, context dissemination, discovery and lookup.
• In order to manipulate context information, it must be represented in some form that is compatible with the models that will be used in the reasoning and situation recognition processes.
• These models could be object oriented, ontological, rule based, logic based, based on semantic graphs or fuzzy logic sets.
• Reasoning mechanisms allow high-level context to be deduced or situations to be recognized that is output of one process can be used as an input to another.

• Reasoning is also used to check the consistency of context and context models.
• It is very important to stress that the prediction does not necessarily anticipates the user wishes or desires, but a possible future that could be interesting for the user.

c. Service Exposure and Control

• The third layer is divided into two main capabilities.
• The first is user-centric and relates to the ability of the user to stay in control of the whole scenario, enabling it to specify when, what, why, who, where and how the data is or can be accessed.
• Through the Human Enabler, users are able to influence the way their behavior is predicted, by controlling how there are being profiled (automatic, off, manually personalized).
• This is essential for establishing and managing trust and for safeguarding privacy, as well as for designing and implementing business security models and policies.
• The second set of features is associated with the capacity of exposing this information (both raw data and inferred one) to third party service providers (such as advertising agencies), through well defined web service interfaces.
• Besides exposing user related information, the human enabler allows data to be subscribed, syndicated or updated on request.

4.3.3. Innovations

The analysis of the first results indicated the following key findings:

• It is possible to infer user behavior based on user preferences, social networks and context-aware systems, with the help of reality/data mining techniques.
• Proximity and Similarity are great weight indicators for inferring influence and can be computed or calculated analytically.
• Both online and offline social networks have influence over a person’s behavior.
• User perceived QoE is improved as the methodology delivers personalization, contextualization, interactivity, adaptation and privacy.
• Users are willing to participate in their own profiling experience and the results are positive.
• Applying these techniques into different fields of computer social sciences may have significant applicability in different parts of the value chain.
  Examples:
  • Infer and suggest missing information in users profile according to his/her peers contextual information.
  • Understand how a specific user can be influenced by another user or community and vice versa.
  • Understand how similar two users are, even if they do not have friends in common.
  
  
  4.4 Privacy in Online Social Networks
  • There is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc.
  • People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment.
  • Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service.
  • Collaborative filtering system is the most popular method in recommender system.
  • The task is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users.
  • Because of the different taste of different people, they rate differently according to their subjective taste.
  • If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster.
  • Performs poor when there is insufficient previous common rating available between users; known as cold start problem
  • To overcome the cold start problem trust based approach to recommendation has emerged.
  • This approach assumes a trust network among users and makes recommendations based on the ratings of the users that are directly or indirectly trusted by the target user.
  • Trust could be used as supplementary or replacement of collaborative filtering system
  • Trust and reputation systems can be used in order to assist users in predicting and selecting the best quality services
  • Binomial Bayesian reputation systems normally take ratings expressed in a discrete binary form as either
  • positive (e.g. good) or
  • negative (e.g. bad).
  • Multinomial Bayesian reputation systems allow the possibility of providing ratings with discrete graded levels such as e.g. mediocre – bad –average – good – excellent
  • Trust models based on subjective logic are directly compatible with Bayesian reputation systems because a bi-jectivemapping exists between their respective trust and reputation representations.
  • This provides a powerful basis for combining trust and reputation systems for assessing the quality of online services.
  • Trust systems can be used to derive local and subjective measures of trust, meaning that different agents can derive different trust in the same entity.
  • Reputation systems compute scores based on direct input from members in the community which is not based on transitivity
  • Bayesian reputation systems are directly compatible with trust systems based on subjective logic, they can be seamlessly integrated. This provides a powerful and flexible basis for online trust and reputation management.
  
  
  Online Social Networks
  • A social network is a map of the relevant ties between the individuals, organizations, nations etc. being studied.
  • With the evolution of digital age, Internet provides a greater scope of implementing social networks online. Online social networks have broader and easier coverage of members worldwide to share information and resources.
  • The first online social networks were called UseNet Newsgroups. designed and built by Duke University graduate students Tom Truscott and Jim Ellis in 1979.
  • Facebook is the largest and most popular online social network at this moment (www.insidefacebook.com).
  • It had 350 million Monthly Active Users (MAU) at the beginning of January 2010. But it has been growing too fast around the world since then.
  • As on 10 February 2010, roughly 23 million more people are using Facebook compared to 30 days ago, many in countries with big populations around the world. This is an interesting shift from much of Facebook’s international growth to date.
  • Once Facebook began offering the service in multiple languages it started blowing up in many countries like Canada, Iceland, Norway, South Africa, Chile, etc.
  • The United States is at the top with more than five million new users; it also continues to be the single largest country on Facebook, with 108 million MAU
   

 

4.5 Trust in Online Environment

• Trust has become important topic of research in many fields including sociology, psychology, philosophy, economics, business, law and IT.
• Trust is a complex word with multiple dimensions.
• Though dozens of proposed definitions are available in the literature, a complete formal unambiguous definition of trust is rare.
• Trust is used as a word or concept with no real definition.
• Trust is such a concept that crosses disciplines and also domains. The focus of definition differs on the basis of the goal and the scope of the projects.
• Two forms
• reliability trust or evaluation trust
• decision trust
• Evaluation trust can be interpreted as the reliability of something or somebody. It can be defined as the subjective probability by which an individual, A, expects that another individual, B, performs a given action on which its welfare depends.
• The decision trust captures broader concept of trust. It can be defined as the extent to which one party is willing to depend on something or somebody in a given situation with a feeling of relative security, even though negative consequences are possible.

4.6 Trust Models Based on Subjective Logic

• Subjective logic is a type of probabilistic logic that explicitly takes uncertainty and belief   ownership into account.
• Arguments in subjective logic are subjective opinions about states in a state space.
• A binomial opinion applies to a single proposition, and can be represented as a Beta distribution. A multinomial opinion applies to a collection of propositions, and can be represented as a Dirichlet distribution.
• Subjective logic defines a trust metric called opinion denoted by  which expresses the relying party A’s belief over a state space X.
• Here  represents belief masses over the states of X, and u represent uncertainty mass where , u €

• Binomial opinions are expressed as where d denotes disbelief in x. When the statement x for example says “David is honest and reliable”, then the opinion can be interpreted as reliability trust in David.
• Let us assume that Alice needs to get her car serviced, and that she asks Bob to recommend a good car mechanic. When Bob recommends David, Alice would like to get a second opinion, so she asks Claire for her opinion about David. This situation is illustrated in Fig. 4.6 a

Fig. 4.6 a Deriving trust from parallel transitive chains

When trust and referrals are expressed as subjective opinions, each transitive trust path

Alice →Bob→ David→ and Alice → Claire → David can be computed with the transitivity operator, where the idea is that the referrals from Bob and Claire are discounted as a function Alice’s trust in Bob and Claire respectively. Finally the two paths can be combined using the cumulative or averaging fusion operator. These operators form part of Subjective Logic and semantic constraints must be satisfied in order for the transitive trust derivation to be meaningful.

• This model is thus both belief-based and Bayesian.
• A trust relationship between A and B is denoted as [A:B]. The transitivity of two arcs is denoted as “:” and the fusion of two parallel paths is denoted as “◊”. The trust network of Fig. 4.6 a can then be expressed as:

[A,D] = ([A,B]:[B,D]) ◊ ([A,C] : [C,D])

• The corresponding transitivity operator for opinions denoted as “” and the corresponding fusion operator as “”. The mathematical expression for combining the

opinions about the trust relationships of Fig. 4.6 a is then:

• Arbitrarily complex trust networks can be analysed with TNA-SL which consists of a network exploration method combined with trust analysis based on subjective logic.
• The method is based on simplifying complex trust networks into a directed series parallel graph (DSPG) before applying subjective logic calculus.