# Efficient Algorithms for Public-Private Social Networks

@article{Chierichetti2015EfficientAF, title={Efficient Algorithms for Public-Private Social Networks}, author={Flavio Chierichetti and Alessandro Epasto and Ravi Kumar and Silvio Lattanzi and Vahab S. Mirrokni}, journal={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, year={2015} }

We introduce the public-private model of graphs. In this model, we have a public graph and each node in the public graph has an associated private graph. The motivation for studying this model stems from social networks, where the nodes are the users, the public graph is visible to everyone, and the private graph at each node is visible only to the user at the node. From each node's viewpoint, the graph is just a union of its private graph and the public graph. We consider the problem of…

## 25 Citations

Algorithms in the Public-Private Model to Sub-Additive functions

- Computer Science, Mathematics
- 2015

The algorithm for the All Pairs Shortest Path(APSP) problem in the Public-Private model is described and details of the experimental analysis of this algorithm run on real-world social network data are given.

Indexing Public-Private Graphs

- Computer ScienceWWW
- 2017

This paper first formulate the problem as asymmetric k-center with outliers, and then gives an efficient and practical algorithm to identify a set of additional visible seed nodes for each user.

PPKWS: An Efficient Framework for Keyword Search on Public-Private Networks

- Computer Science, Economics2020 IEEE 36th International Conference on Data Engineering (ICDE)
- 2020

A new keyword search framework, called public-private keyword search (PPKWS), which is verified through experiments that the algorithms implemented on top of PPKWS run 113 times faster than the original algorithms directly running on the public network attached to the private network for retrieving answers that spans through them.

PP-DBLP: Modeling and Generating Attributed Public-Private Networks with DBLP

- Computer Science2018 IEEE International Conference on Data Mining Workshops (ICDMW)
- 2018

This paper generates four public-private networks from real-world DBLP records, called PP-DBLP, and proposes an advanced model of attributed public- private graphs where vertices have not only private edges but also private attributes, which is motivated by widely existing attributed graphs.

Community Search over Big Graphs: Models, Algorithms, and Opportunities

- Computer Science2017 IEEE 33rd International Conference on Data Engineering (ICDE)
- 2017

This tutorial surveys the state-of-the-art of community search on various kinds of networks across different application areas such as densely-connected community search, attributedcommunity search, social circle discovery, and querying geosocial groups.

Estimating the Number of Induced Subgraphs from Incomplete Data and Neighborhood Queries

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- 2021

This work investigates how to efficiently estimate the number of small subgraphs based on full access to one or two noisy and incomplete samples of a large underlying network and on few queries revealing the neighborhood of carefully selected vertices.

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- Computer ScienceThe VLDB Journal
- 2019

This survey conducts a thorough review of existing community search works, analyzes and compares the quality of communities under their models, and the performance of different solutions, and points out new research directions.

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- Computer ScienceInternational Journal of Data Science and Analytics
- 2015

Main finding includes that different network structures have different epidemic thresholds and the node influence can identify influential nodes that the existing centrality measures cannot and the network structure is systematically changed using synthetically generated networks.

Fast Distributed Submodular Cover: Public-Private Data Summarization

- Computer ScienceNIPS
- 2016

A fast distributed algorithm for submodular cover, FASTCOVER, is developed that provides a succinct summary of massive dataset in one shot and for all users and is competitive with that of the centralized algorithm with the number of rounds that is exponentially smaller than state of the art results.

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- 2017

A search network model for social network evolution is proposed, it is proved that the degree distribution follows an intensified power-law, and the network diameter shrinks, and it is quantitatively shown that the search engine accelerates the rumor propagation in social networks.

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