S. R. S. Iyengar

A Faster Algorithm to Update Betweenness Centrality after Node Alteration

Betweenness centrality is a centrality measure that is widely used, with applications across several disciplines. It is a measure which quantifies the importance of a vertex based on its occurrence in shortest paths between all possible pairs of vertices in a graph. This is a global measure, and in order to find the betweenness centrality of a node, one is supposed to have complete information about the graph. Most of the algorithms that are used to find betwenness centrality assume the constancy of the graph and are not efficient for dynamic networks. We propose a technique to update betweenness centrality of a graph when nodes are added or deleted. Our algorithm experimentally speeds up the calculation of betweenness centrality (after updation) from 7 to 412 times, for real graphs, in comparison to the currently best known technique to find betweenness centrality.

Understanding Spreading Patterns on Social Networks Based on Network Topology

Ever since the introduction of the first epidemic model, scientists have tried extrapolating the damage caused by a contagious disease, given its spreading pattern in the premature stage. However, understanding epidemiology remains an elusive mystery to researchers specifically because of the unavailability of large amount of data. We utilise the study of diffusion of memes in a social networking website to solve this problem. In this paper, we analyse the impact of specific meso-scale properties of a network on a meme traversing over it. We have employed SCCP (Scale free, Communities, Core Periphery structure) networks for analysis purpose. We propose a new meme propagation model for real world social networks and observe the cause of virality of a meme. We have tested and validated our model with the real world information spreading pattern.

Modeling Memetics Using Edge Diversity

The study of meme propagation and the prediction of meme trajectory are emerging areas of interest in the field of complex networks research. In addition to the properties of the meme itself, the structural properties of the underlying network decides the speed and the trajectory of the propagating meme. In this paper, we provide an artificial framework for studying the meme propagation patterns. Firstly, the framework includes a synthetic network which simulates a real world network and acts as a testbed for meme simulation. Secondly, we propose a meme spreading model based on the diversity of edges in the network. Through the experiments conducted, we show that the generated synthetic network combined with the proposed spreading model is able to simulate a real world meme spread. Our proposed model is validated by the propagation of the Higgs boson meme on Twitter as well as many real world social networks.

Secure multiparty graph computation

The recent explosion of online networked data and the discovery of universal topological characteristics in real world networks has led to the emergence of a new domain of research, namely, social networks. However, much research in this domain remains unexplored due to the inaccessibility of data of sensitive networks, which include hate networks, trust networks and sexual relationship networks. This paper proposes a secure multiparty protocol which allows a set of parties to compute the underlying network on them. The proposed protocol is information theoretically secure, and its security is further enhanced by a list of security tests, which includes k-anonymity test, check for self loops and weighted edges. Although some solutions have been proposed for this problem earlier, the practicality of each one of those is questionable.

Presence of an Ecosystem: a Catalyst in the Knowledge Building Process in Crowdsourced Annotation Environments

The phenomenal success of certain crowdsourced online platforms, such as Wikipedia, is accredited to their ability to tap the crowds potential to collaboratively build knowledge. While it is well known that the crowds collective wisdom surpasses the cumulative individual expertise, little is understood on the dynamics of knowledge building in a crowdsourced environment. A proper understanding of the dynamics of knowledge building in a crowdsourced environment would enable one in the better designing of such environments to solicit knowledge from the crowd. Our experiment on crowdsourced systems based on annotations shows that an important reason for the rapid knowledge building in such environments is due to variance in expertise. We use, as our test bed, a customized Crowdsourced Annotation System (CAS) which provides a group of users the facility to annotate a given document while trying to understand it. Our results show the presence of different genres of proficiency amongst the users of an annotation system. We observe that the crowdsourced knowledge ecosystem comprises of mainly four categories of contributors, namely: Probers, Solvers, Articulators and Explorers. We infer from our experiment that the knowledge garnering mainly happens due to the synergetic interaction across these categories.

Evolving models for meso-scale structures

Real world complex networks are scale free and possess meso-scale properties like core-periphery and community structure. We study evolution of the core over time in real world networks. This paper proposes evolving models for both unweighted and weighted scale free networks having local and global core-periphery as well as community structure. Network evolves using topological growth, self growth, and weight distribution function. To validate the correctness of proposed models, we use K-shell and S-shell decomposition methods. Simulation results show that the generated unweighted networks follow power law degree distribution with droop head and heavy tail. Similarly, generated weighted networks follow degree, strength, and edge-weight power law distributions. We further study other properties of complex networks, such as clustering coefficient, nearest neighbor degree, and strength degree correlation.

Estimating the degree centrality ranking

Complex networks have gained more attention from the last few years. The size of the real world complex networks, such as online social networks, WWW networks, collaboration networks, is exponentially increasing with time. It is not feasible to completely collect, store and process these networks. In the present work, we propose a method to estimate degree centrality ranking of a node without having complete structure of the graph. The proposed method uses degree of a node and power law exponent of the degree distribution to calculate the ranking. We also study simulation results on Barabasi-Albert model. Simulation results show that average error in the calculated ranking is approximately 5% of total number of nodes.

Secure Multiparty Construction of a Distributed Social Network

The advancement in technology has resulted in a better connected society. These connections foster social interactions that result in an emergent structure. This structure is popularly termed as a social network and is an integral component of study, in the field of network science. However, the study of these social networks is limited to the availability of data on the underlying social interactions. Privacy concerns restrict the access to network data with sensitive information. Networks that capture the relations such as trust, enmity, sexual contact, are a few examples of sensitive networks. A study of these sensitive networks is important in unraveling the behavioral aspects of the concerned individuals. The current paper proposes a multiparty computation algorithm that allows the construction of an unlabeled random isomorphic version of a distributedly held network. The protocol is proven to be secure in the presence of the extended arithmetic black-box, which supports the operations of addition, multiplication, comparison and equality checks.

Pseudo-Cores: The Terminus of an Intelligent Viral Meme’s Trajectory

Comprehending the virality of a meme can help us in addressing the problems pertaining to disciplines like epidemiology and digital marketing. Therefore, it is not surprising that memetics remains a highly analyzed research topic ever since the mid 1990s. Some scientists choose to investigate the intrinsic contagiousness of a meme while others study the problem from a network theory perspective. In this paper, we revisit the idea of a core-periphery structure and apply it to understand the trajectory of a viral meme in a social network. We have proposed shell-based hill climbing algorithms to determine the path from a periphery shell(where the meme originates) to the core of the network. Further simulations and analysis on the networks behavioral characteristics helped us unearth specialized shells which we term Pseudo-Cores. These shells emulate the behavior of the core in terms of size of the cascade triggered. In our experiments, we have considered two sets for the target nodes, one being core and the other being any of the pseudo-core. We compare our algorithms against already existing path finding algorithms and validate the better performance experimentally.

Computing Betweenness Centrality: An Efficient Privacy-Preserving Approach.

Betweenness centrality is a classic network measure used to determine prominent nodes in a network G(V, E), where the edges capture a type of flow through the network (like information, material or money). Betweenness being a global centrality measure requires the entire network information to compute the centrality of even a single vertex. We consider the setting where the global network structure is not present centrally with a single individual. Rather, the data is distributed among many individuals, each having only a partial view of the network. Furthermore, confidentiality constraints prevent the individual parties from disclosing their share of the data, thus inhibiting the aggregation of the entire network for analysis. The current paper proposes a secure multiparty protocol to compute the betweenness centrality measure, in a privacy preserving manner, for the considered setting. Employing various optimizations, including oblivious data structures and oblivious RAM, we present a secure variant of the Brandes algorithm for computing betweenness centrality in unweighted networks. The protocol is designed in the semi-honest adversarial model under the two-party setting. We evaluate the performance of the designed protocol by implementing them in the Obliv-C framework for secure computation. We are the first to provide a benchmark for the implementations using the state of the art ORAM schemes and help identify the best schemes for input data of different sizes. Employing the Circuit ORAM and the Square-Root ORAM schemes, we report the complexity of the proposed protocol as \(\mathcal {O}(|V||E|\log ^3|E|)\) and \(\mathcal {O}(|V||E|^{1.5}\log ^{1.5}|E|)\) primitive operations respectively. The asymptotic complexity of Circuit ORAM is found to be the least, with an overhead of only \(\mathcal {O}(\log ^3|E|)\) compared to the traditional non-oblivious Brandes algorithm with complexity \(\mathcal {O}(|V||E|)\).