Sumit Negi(Xerox Research); Abhimanyu Jaju (IIT Kanpur) ; Santanu Chaudhury (IIT Delhi)

Promoting visual diversity in image search results has been of immense interest in the MIR (multimedia information retrieval) community. However, most of the work done in this space and the test collections used therein have ignored the use of social tags (user generated metadata) for image search result diversification on social multimedia platforms such as Flickr, Picasa etc. Unlike traditional multimedia content, content generated on such social media platforms are usually annotated with a rich set of explicit and implicit human generated metadata (referred here as social tags) such as keywords, textual description, category information, author's profile, user-to-user and user-to-content interaction etc which can be useful for the image search result diversity task. In this paper we demonstrate how existing image search result diversification method can be extended to incorporate social tag information. Experiments on a real-world dataset shows that incorporating social tag features in some of the popular diversification algorithms results in improvement over baseline numbers.

Kundan Kandhway (IISc Bangalore); Bhushan Kotnis (IISc Bangalore)

Participatory crowd sensing social systems rely on the participation of large number of individuals. Since humans are strategic by nature, effective incentive mechanisms are needed to encourage participation. A popular mechanism to recruit individuals is through referrals and passive incentives such as geometric incentive mechanisms used by the winning team in the 2009 DARPA Network Challenge and in multi level marketing schemes. The effect of such recruitment schemes on the effort put in by recruited strategic individuals is not clear. This paper attempts to fill this gap. Given a referral tree and the direct and passive reward mechanism, we formulate a network game where agents compete for finishing crowd sensing tasks. We characterize the Nash equilibrium efforts put in by the agents and derive closed form expressions for the same. We discover free riding behavior among nodes who obtain large passive rewards. This work has implications on designing effective recruitment mechanisms for crowd sourced tasks. For example, usage of geometric incentive mechanisms to recruit large number of individuals may not result in proportionate effort because of free riding.

Parisutham Nirmala (PSG College of Technology); R Nadarajan (PSG College of Technology)

With the increasing scale and complexity of today's communication network, it is becoming more essential to find the frequent subgraph of a time series of communication network to predict the nodes which are often involved in the communication. This knowledge is useful in devising new routing algorithms, and understanding the frequent communication patterns. It is often required to determine the frequency of a query graph in the graph database to realize how much that query graph is occurred in the communication network and it facilitates to monitor the performance of the nodes which are presented in the query graph. When a query graph does not have an exact match with the graphs in the graph database, the idea of finding approximate match of the query graph is coined to determine the frequency of the query graph by relaxing few edges of it. This paper presents an algorithm which decides whether the query graph is frequent as expected times in the graph database, if not so then the algorithm relax infrequent edges from the query graph to find the subgraph of the query graph with the given minimum support threshold.

Mithileysh Sathiyanarayanan (University of Brighton); Donato Pirozzi (Università degli Studi di Salerno)

As social network information keeps growing, there is always a need for better information visualization techniques for carrying out multifarious analysis, which is commonly called as social network analysis. Social networks have become a big platform for advertisements, where a company targets highly connected than to more isolated users. To target highly connected users and their activities, visualizations such as Euler diagrams, Treemap diagrams, graphs and their combinations were tested by various researchers. Though Euler diagrams combined with graph and Treemap diagrams with graph have provided some interesting results, the one question yet to be answered is the scalability. In this paper, we propose a novel visual technique, ""Spherule diagrams with graph"", which addresses the scalability issue. The novel technique is then compared with the traditional Euler diagrams with graph using a twitter case study in an empirical form. Twenty-eight participants were exposed to eighteen diagrams (nine diagrams of each type of visualization) in a software which recorded accuracy and response time (i.e., performance measure). The results of 504 observations indicate that (a) there is no significant difference between the visualizations in terms of accuracy and (b) there is a significant difference between the visualizations in terms of response time. Also, users were asked to aggrandize between the two visualizations (i.e., preference measure), where 75% preferred Spherule diagrams with graph for its simplicity, comprehensiveness, navigation, alignment (layout), set ordering and data items ordering characteristics. We conclude that, Spherule diagrams with graph will be beneficial for researchers and practitioners in the information visualization community who are exploring social networks for business.

Siddhartha Sarma (IISc Bangalore); Kundan Kandhway (IISc Bangalore); Bhushan Kotnis (IISc Bangalore); Joy Kuri (IISc Bangalore)

We study a budget allocation problem for urban monitoring (e.g., pollution, litter etc.) using participatory sensing. The human participants and locations of phenomena to be sensed are modeled as Poisson point processes. The decision maker (e.g., city authority) has to decide the split of budget among strategies---advertisements to recruit participants, and providing rewards to those participants for their efforts in reporting the events. We propose and analyse three schemes for rewarding participants---equal, proportional and probabilistic. The probabilistic scheme exploits risk seeking behavior of participants, which is a typical human psychological behavior studied in the Prospect theory. Analytical and numerical study of the proposed schemes uncovers useful insights such as, for the equal reward scheme, advertisement for recruiting more individuals is preferable than increasing rewards for the recruited individuals. However, the opposite is true for the other two schemes.

Karthik Gopalakrishnan (IIT Patna); Arun Pandey (IIT Patna); Joydeep Chandra (IIT Patna)

Online social networking sites such as Facebook, Twitter and Flickr are among the most popular sites on the Web, providing platforms for sharing information and interacting with a large number of people. The different ways for users to interact, such as liking, retweeting and favoriting user-generated content, are among the defining and extremely popular features of these sites. While empirical studies have been done to learn about the network growth processes in these sites, few studies have focused on social interaction behaviour and the effect of social interaction on network growth.In this paper, we analyze large-scale data collected from the Flickr social network to learn about individual favoriting behaviour and examine the occurrence of link formation after a favorite is created. We do this using a systematic formulation of Flickr as a two-layer temporal multiplex network: the first layer describes the follow relationship between users and the second layer describes the social interaction between users in the form of favorite markings to photos uploaded by them. Our investigation reveals that (a) favoriting is well-described by preferential attachment, (b) over 50% of favorites are reciprocated within 10 days if at all they are reciprocated, (c) different kinds of favorites differ in how fast they are reciprocated, and (d) after a favorite is created, multiplex triangles are closed by the creation of follow links by the favoriter's followers to the favorite receiver.

Yayati Gupta (IIT Ropar); Sudarshan Iyengar (IIT Ropar); Nidhi Sridhar (NIT Suratkal); Jaspal Singh (IIT Ropar)

Social Networking Sites (SNSs) are powerful marketing and communication tools. There are hundreds of SNSs that have entered and exited the market over time. The coexistence of multiple SNSs is a rarely observed phenomena. Most coexisting SNSs either serve different purposes for its users or have cultural differences among them. The introduction of a new SNS with a better set of features can lead to the demise of an existing SNS, as observed in the transition from Orkut to Facebook. The paper proposes a model for analyzing the transition of users from one SNS to another, when a new SNS is introduced in the system. The game theoretic model proposed considers two major factors in determining the success of a new SNS. The first being time that an old SNS gets to stabilise. We study whether the time that a SNS like Facebook received to monopolize its reach had a distinguishable effect. The second factor is the set of features showcased by the new SNS. The results of the model are also experimentally verified with data collected by means of a survey.