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Temporal Graph Mining and Distributed Processing

par Kumar, Rohit
Président du jury Zimanyi, Esteban
Promoteur Calders, Toon ;Abelló, Alberto
Publication Non publié, 2018-06-19

Thèse de doctorat

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Résumé :

With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g., emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction.We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data.Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of these algorithms is to correctly partition the graph data between different machines. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.