Billions of users in OSNs are tweeting and sharing their personal statuses daily without being aware of where that information eventually travels to. Likewise, a huge magnitude of data available on OSNs poses a substantial challenge to track how a piece of information leaks to specific targets and how to give users fine-grained control over sharing. We formulate and investigate the problem of constructing a sharing circle on the y so that OSN users can safely share their information as intended. That is to guarantee the risk of leaking the information to a set of unwanted targets to be less than desired thresholds, while maximizing the visibility of the information to the friends. We propose both provably good algorithms and novel hybridizing heuristics for the problem and perform multiple experiments in real-world traces to highlight several important observations, which help to sharpen the security of OSNs in the future.

Sybil attack and misinformation spreading are two crucial problems that recently occur in communication networks, especially in OSNs. In sybil attack, sybil nodes with multiple fake identities are trying to attain and then influence the others as if they are honest ones, as in recommendation systems or online votes. In the other issue, the spread out of misinformation in a social network can lead to an undesired reaction in the wide public. These two problems are very challenging due to the huge network scale and the unprediction of social influence.

Communication networks play a vital role in the day-to-day routine of all sectors of our society. Unfortunately, these systems are often greatly affected by several uncertain factors, including external natural or man-made interferences (e.g., severe weather and enemy/malicious attacks.) The failure of a few key nodes that play a vital role in maintaining the network’s connectivity can break down its operation.

The complexity of networked systems and their interdependencies can operate in conjunction to amplify the negative effects of external disruptions. In a cascading failure, a failure of a part can trigger the failure of successive parts. Such a failure may happen in many types of systems, including power transmission, computer networking, finance and bridges. We investigate how vulnerability propagates under different diffusion models, network structures, and coupling models. Our research provides multiple insights into how to design robust interdependent systems as well as how to mitigate such cascading-failures.

Social network applications on Facebook, MySpace, Bebo, etc. are excellent examples of viral marketing. The spreading of an application starts with one user installing the application, then the application sends ‘Invitations’ to all friends of the user. Furthermore, for every activity of the application the application will notify friends with a mini-story or feed. In turn, friends of the users get curious about the application, install it and continue the exponentially viral growing process. The more initial users selected, the faster the application will spread through the network. However, due to limits on resources companies often want to target a small group of the most influential users so that after a chain-reaction of influence the company can reach to users in the whole network or a segment of the network.

Community structure is defined as a subgraph such that there is a higher density of edges within the subgraph than between them. This has applications in many domains, not only in computer networks, but also in computational biology, social research, life sciences and physics. We focuses on complex, dynamic, and evolving over time, yet often greatly affected by uncertain factors, which may arise in many forms, including natural or man-made interferences.

Many problems in reality take the forms of complex networks and their underlying organization exhibit the property of containing communities, i.e. groups of tightly internally-connected and sparsely externally-connected nodes in the network structure. Community detection is the problem of identifying those communities in a given network with or without extra information such as the number of communities, and with overlapping or non-overlapping communities.

Broadcast has been a fundamental mechanism to lower down delivery time latency in wireless ad hoc networks. The intrinsic broadcasting nature of radio communications can either speed up the communications by transmitting the message to all neighbors or slow down the communications because of the conflicts with other transmissions. Thus, it is crucial to devise the conflict-free broadcast schedule, especially in mobile ad hoc networks on 3D space. Additionally, as most real networks are dynamic, it is also challenging to develop online algorithms for the broadcast scheduling with a good performance.