Publications by Ehsan Kazemi | EPFL Graph Search

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Networks are central in the modeling and analysis of many large-scale human and technical systems, and they have applications in diverse fields such as computer science, biology, social sciences, and economics. Recently, network mining has been an active a ...

Network alignment refers to the problem of matching the vertex sets of two unlabeled graphs, which can be viewed as a generalization of the classic graph isomorphism problem. Network alignment has applications in several fields, including social network an ...

We present the work that allowed us to win the Next-Place Prediction task of the Nokia Mobile Data Challenge. Using data collected from the smartphones of 80 users, we explore the characteristics of their mobility traces. We then develop three families of ...

Location-aware smartphones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to track users. We address this probl ...

Mobility is a central aspect of our life; the locations we visit reflect our tastes and lifestyle and shape our social relationships. The ability to foresee the places a user will visit is therefore beneficial to numerous applications, ranging from forecas ...

In many graph–mining problems, two networks from different domains have to be matched. In the absence of reliable node attributes, graph matching has to rely on only the link structures of the two networks, which amounts to a generalization of the classic ...

Ehsan Kazemi

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Network Alignment: Theory, Algorithms, and Applications

When Can Two Unlabeled Networks Be Aligned Under Partial Overlap?

Where to go from here? Mobility prediction from instantaneous information

Hiding in the Mobile Crowd: Location Privacy through Collaboration

Been There, Done That: What Your Mobility Traces Reveal about Your Behavior

Growing a Graph Matching from a Handful of Seeds

Mitigating Epidemics through Mobile Micro-measures