1 citations · 3 across the 19 of their papers we have counts for
10 papers · 1 filter
Fair Top-k Katz Centrality via Graph Design
Ivan Qin, Prudence Wong, Lutz Oettershagen
Centrality measures are widely used to rank nodes in networked data, but fairness interventions for graph centrality typically target global score mass or modify the centrality ope…
Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Lutz Oettershagen, Othon Michail
Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the \emph{Fair Minimum Labeling} (FML) problem:…
Consistent Tie-Strength Labeling for Multilayer Strong Triadic Closure
Lutz Oettershagen, Athanasios L. Konstantinidis, Fariba Ranjbar +1
Inferring tie strengths (strong vs. weak) is a core task in network analysis, often guided by the Strong Triadic Closure (STC) principle. In multilayer networks, such as social pla…
Finding Densest Subgraphs with Edge-Color Constraints
Lutz Oettershagen, Honglian Wang, Aristides Gionis
We consider a variant of the densest subgraph problem in networks with single or multiple edge attributes. For example, in a social network, the edge attributes may describe the ty…
An Edge-Based Decomposition Framework for Temporal Networks
Lutz Oettershagen, Athanasios L. Konstantinidis, Giuseppe F. Italiano
A temporal network is a dynamic graph where every edge is assigned an integer time label that indicates at which discrete time step the edge is available. We consider the problem o…
A Higher-Order Temporal H-Index for Evolving Networks
Lutz Oettershagen, Nils M. Kriege, Petra Mutzel
The H-index of a node in a static network is the maximum value such that at least of its neighbors have a degree of at least . Recently, a generalized version, the -t…
A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks
Lutz Oettershagen, Nils M. Kriege, Claude Jordan +1
We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs. Such dissemination processes can be spreading (fake) news, infe…
Inferring Tie Strength in Temporal Networks
Lutz Oettershagen, Athanasios L. Konstantinidis, Giuseppe F. Italiano
Inferring tie strengths in social networks is an essential task in social network analysis. Common approaches classify the ties as wea} and strong ties based on the strong triadic…
Temporal Walk Centrality: Ranking Nodes in Evolving Networks
Lutz Oettershagen, Petra Mutzel, Nils M. Kriege
We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network. In contrast…
Temporal Graph Kernels for Classifying Dissemination Processes
Lutz Oettershagen, Nils M. Kriege, Christopher Morris +1
Many real-world graphs or networks are temporal, e.g., in a social network persons only interact at specific points in time. This information directs dissemination processes on the…