Search Results for author:
Found 23 papers, 14 papers with code
Diversity-aware clustering: Computational Complexity and Approximation Algorithms
We present parameterized approximation algorithms with approximation ratios $1+ \frac{2}{e}$, $1+\frac{8}{e}$ and $3$ for diversity-aware $k$-median, diversity-aware $k$-means and diversity-aware $k$-supplier, respectively.
Rebalancing Social Feed to Minimize Polarization and Disagreement
Social media have great potential for enabling public discourse on important societal issues.
Adversaries with Limited Information in the Friedkin--Johnsen Model
To address this concern, we raise the following question: Can an attacker sow discord in a social network, even when only the network topology is known?
Learning Cellular Coverage from Real Network Configurations using GNNs
Cellular coverage quality estimation has been a critical task for self-organized networks.
Concise and interpretable multi-label rule sets
Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability.
Regularized impurity reduction: Accurate decision trees with complexity guarantees
In this paper, we justify the use of a general family of impurity functions, including the popular functions of entropy and Gini-index, in scenarios where small trees are desirable, by showing that a simple enhancement can equip them with complexity guarantees.
Open World Learning Graph Convolution for Latency Estimation in Routing Networks
Accurate routing network status estimation is a key component in Software Defined Networking.
Generalized Leverage Scores: Geometric Interpretation and Applications
In particular, leverage scores, which relate the columns of a matrix to the subspaces spanned by its leading singular vectors, are helpful in revealing column subsets to approximately factorize a matrix with quality guarantees.
Ranking with submodular functions on a budget
In more detail, given a set of items and a set of non-decreasing submodular functions, where each function is associated with a budget, we aim to find a ranking of the set of items that maximizes the sum of values achieved by all functions under the budget constraints.
Improved analysis of randomized SVD for top-eigenvector approximation
We present a novel analysis of the randomized SVD algorithm of \citet{halko2011finding} and derive tight bounds in many cases of interest.
Discovering conflicting groups in signed networks
In this paper we study the problem of detecting $k$ conflicting groups in a signed network.
Co-exposure Maximization in Online Social Networks
Social media has created new ways for citizens to stay informed on societal matters and participate in political discourse.
Diverse Rule Sets
Thus, interpretability of models has been recognized as an equally important quality as their predictive power.
Searching for polarization in signed graphs: a local spectral approach
In this paper we formulate the problem of finding local polarized communities in signed graphs as a locally-biased eigen-problem.
Finding path motifs in large temporal graphs using algebraic fingerprints
In particular, given a vertex-colored temporal graph and a multiset of colors as a query, we search for temporal paths in the graph that contain the colors specified in the query.
What is the dimension of your binary data?
Defining the effective dimensionality of such a dataset is a nontrivial problem.
Explainable time series tweaking via irreversible and reversible temporal transformations
We show that the problem is NP-hard, and focus on two instantiations of the problem, which we refer to as reversible and irreversible time series tweaking.
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own.
Social and Information Networks
Balancing information exposure in social networks
Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information exposure for the two campaigns is balanced.
Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter
We model the problem of learning the liberal-conservative ideology space of social media users and media sources as a constrained non-negative matrix-factorization problem.
Social and Information Networks
Semi-supervised Kernel Metric Learning Using Relative Comparisons
We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items.
Quantifying Controversy in Social Media
Unlike previous work, rather than study controversy in a single hand-picked topic and use domain specific knowledge, we take a general approach to study topics in any domain.
Social and Information Networks
DopeLearning: A Computational Approach to Rap Lyrics Generation
First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines.
