• Publications
  • Influence
Learning influence probabilities in social networks
TLDR
This paper proposes models and algorithms for learning the model parameters and for testing the learned models to make predictions, and develops techniques for predicting the time by which a user may be expected to perform an action. Expand
Exploratory mining and pruning optimizations of constrained associations rules
TLDR
An architecture that opens up the black-box, and supports constraint-based, human-centered exploratory mining of associations, and introduces and analyzes two properties of constraints that are critical to pruning: anti-monotonicity and succinctness. Expand
CELF++: optimizing the greedy algorithm for influence maximization in social networks
TLDR
This work proposes CELF++ and empirically show that it is 35-55% faster than CELF and proposes the CELF algorithm for tackling the second major source of inefficiency of the basic greedy algorithm. Expand
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model
  • A. Goyal, Wei Lu, L. Lakshmanan
  • Mathematics, Computer Science
  • IEEE 11th International Conference on Data Mining
  • 11 December 2011
TLDR
This paper proposes Simpath, an efficient and effective algorithm for influence maximization under the linear threshold model that addresses these drawbacks by incorporating several clever optimizations, and shows that Simpath consistently outperforms the state of the art w.r.t. running time, memory consumption and the quality of the seed set chosen. Expand
Information and Influence Propagation in Social Networks
TLDR
A detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena are described as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. Expand
On approximating optimum repairs for functional dependency violations
TLDR
An approximation algorithm is presented that for a fixed set of functional dependencies and an arbitrary input inconsistent database, produces a repair whose distance to the database is within a constant factor of the optimum repair distance. Expand
A Data-Based Approach to Social Influence Maximization
TLDR
A new model is introduced, which is called credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread, and is time-aware in the sense that it takes the temporal nature of influence into account. Expand
Mining frequent itemsets with convertible constraints
TLDR
A notion of convertible constraints is developed and systematically analyzed, classify, and characterize this class and techniques which enable them to be readily pushed deep inside the recently developed FP-growth algorithm for frequent itemset mining are developed. Expand
TIMBER: A native XML database
TLDR
The overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan is described, believing that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store. Expand
Minimization of tree pattern queries
TLDR
A fast algorithm is presented, CDM, that identifies and eliminates local redundancies due to ICs, based on propagating “information labels” up the tree pattern, and shows the surprising result that the algorithm obtained by first augmenting the tree patterns using ICS, and then applying CIM, always finds the unique minimal equivalent query. Expand
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