Thang N. Dinh

Adaptive algorithms for detecting community structure in dynamic social networks

Social networks exhibit a very special property: community structure. Understanding the network community structure is of great advantages. It not only provides helpful information in developing more social-aware strategies for social network problems but also promises a wide range of applications enabled by mobile networking, such as routings in Mobile Ad Hoc Networks (MANETs) and worm containments in cellular networks. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social activities and interactions are evolving rapidly. Can we quickly and efficiently identify the network community structure? Can we adaptively update the network structure based on previously known information instead of recomputing from scratch? In this paper, we present Quick Community Adaptation (QCA), an adaptive modularity-based method for identifying and tracing community structure of dynamic online social networks. Our approach has not only the power of quickly and efficiently updating network communities, through a series of changes, by only using the structures identified from previous network snapshots, but also the ability of tracing the evolution of community structure over time. To illustrate the effectiveness of our algorithm, we extensively test QCA on real-world dynamic social networks including ENRON email network, arXiv e-print citation network and Facebook network. Finally, we demonstrate the bright applicability of our algorithm via a realistic application on routing strategies in MANETs. The comparative results reveal that social-aware routing strategies employing QCA as a community detection core outperform current available methods.

Overlapping communities in dynamic networks: their detection and mobile applications

Many practical problems on Mobile networking, such as routing strategies in MANETs, sensor reprogramming in WSNs and worm containment in online social networks (OSNs) share an ubiquitous, yet interesting feature in their organizations: community structure. Knowledge of this structure provides us not only crucial information about the network principles, but also key insights into designing more effective algorithms for practical problems enabled by Mobile networking. However, understanding this interesting feature is extremely challenging on dynamic networks where changes to their topologies are frequently introduced, and especially when network communities in reality usually overlap with each other. We focus on the following questions (1) Can we effectively detect the overlapping community structure in a dynamic network? (2) Can we quickly and adaptively update the network structure only based on its history without recomputing from scratch? (3) How does the detection of network communities help mobile applications? We propose AFOCS, a two-phase framework for not only detecting quickly but also tracing effectively the evolution of overlapped network communities in dynamic mobile networks. With the great advantages of the overlapping community structure, AFOCS significantly helps in reducing up to 7 times the infection rates in worm containment on OSNs, and up to 11 times overhead while maintaining good delivery time and ratio in forwarding strategies in MANETs.

On new approaches of assessing network vulnerability: hardness and approximation

Society relies heavily on its networked physical infrastructure and information systems. Accurately assessing the vulnerability of these systems against disruptive events is vital for planning and risk management. Existing approaches to vulnerability assessments of large-scale systems mainly focus on investigating inhomogeneous properties of the underlying graph elements. These measures and the associated heuristic solutions are limited in evaluating the vulnerability of large-scale network topologies. Furthermore, these approaches often fail to provide performance guarantees of the proposed solutions. In this paper, we propose a vulnerability measure, pairwise connectivity, and use it to formulate network vulnerability assessment as a graph-theoretical optimization problem, referred to as -disruptor. The objective is to identify the minimum set of critical network elements, namely nodes and edges, whose removal results in a specific degradation of the network global pairwise connectivity. We prove the NP-completeness and inapproximability of this problem and propose an pseudo-approximation algorithm to computing the set of critical nodes and an pseudo-approximation algorithm for computing the set of critical edges. The results of an extensive simulation-based experiment show the feasibility of our proposed vulnerability assessment framework and the efficiency of the proposed approximation algorithms in comparison to other approaches.

On Approximation of New Optimization Methods for Assessing Network Vulnerability

Assessing network vulnerability before potential disruptive events such as natural disasters or malicious attacks is vital for network planning and risk management. It enables us to seek and safeguard against most destructive scenarios in which the overall network connectivity falls dramatically. Existing vulnerability assessments mainly focus on investigating the inhomogeneous properties of graph elements, node degree for example, however, these measures and the corresponding heuristic solutions can provide neither an accurate evaluation over general network topologies, nor performance guarantees to large scale networks. To this end, in this paper, we investigate a measure called pairwise connectivity and formulate this vulnerability assessment problem as a new graph-theoretical optimization problem called β-disruptor, which aims to discover the set of critical node/edges, whose removal results in the maximum decline of the global pairwise connectivity. Our results consist of the NP-Completeness and inapproximability proof of this problem, an O(log n loglog n) pseudo-approximation algorithm for detecting the set of critical nodes and an O(log^1.5 n) pseudo-approximation algorithm for detecting the set of critical edges. In addition, we devise an efficient heuristic algorithm and validate the performance of the our model and algorithms through extensive simulations.

Towards social-aware routing in dynamic communication networks

Many communication networks such as Mobile Ad Hoc Networks (MANETs) involve in human interactions and exhibit properties of social networks. Hence, it is interesting to see how knowledge from social networks can be used to enhance the communication processes. We focus on the use of identifying modular structure in social networks to improve the efficiency of routing strategies. Since nodes mobility in a network often alters its modular structure and requires recomputing of modules from scratch, updating the modules is the main bottleneck in current social-aware routing strategies where nodes often have limited processing speed. Towards real-time routing strategies, we develop an adaptive method to efficiently update modules in a dynamic network in which a novel compact representation of the network is used to significantly reduces the network size while preserving essential network structure.

Maximizing the Spread of Positive Influence in Online Social Networks

Online social networks (OSNs) provide a new platform for product promotion and advertisement. Influence maximization problem arisen in viral marketing has received a lot of attentions recently. Most of the existing diffusion models rely on one fundamental assumption that an influenced user necessarily adopts the product and encourages his/her friends to further adopt it. However, an influenced user may be just aware of the product. Due to personal preference, neutral or negative opinion can be generated so that product adoption is uncertain. Maximizing the total number of influenced users is not the uppermost concern, instead, letting more activated users hold positive opinions is of first importance. Motivated by above phenomenon, we proposed a model, called Opinion-based Cascading (OC) model. We formulate an opinion maximization problem on the new model to take individual opinion into consideration as well as capture the change of opinions at the same time. We show that under the OC model, opinion maximization is NP-hard and the objective function is no longer submodular. We further prove that there does not exist any approximation algorithm with finite ratio unless P=NP. We have designed an efficient algorithm to compute the total positive influence based on this new model. Comprehensive experiments on real social networks are conducted, and results show that previous methods overestimate the overall positive influence, while our model is able to distinguish between negative opinions and positive opinions, and estimate the overall influence more accurately.

Cost-effective viral marketing for time-critical campaigns in large-scale social networks

Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, “word-of-mouth” exchanges, so-called viral marketing, in social networks can be used to increase product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How do we economically spend more resources to increase the spreading speed? We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on large-scale networks. VirAds guarantees a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics that realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(logn) is unlikely possible.

Dynamic social community detection and its applications.

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.

On the approximability of positive influence dominating set in social networks

In social networks, there is a tendency for connected users to match each other’s behaviors. Moreover, a user likely adopts a behavior, if a certain fraction of his family and friends follows that behavior. Identifying people who have the most influential effect to the others is of great advantages, especially in politics, marketing, behavior correction, and so on. Under a graph-theoretical framework, we study the positive influence dominating set (PIDS) problem that seeks for a minimal set of nodes

Cost-aware Targeted Viral Marketing in billion-scale networks

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