72 citations · 760 across the 54 of their papers we have counts for
26 papers · 1 filter
Jaccard-constrained dense subgraph discovery
Chamalee Wickrama Arachchi, Nikolaj Tatti
Finding dense subgraphs is a core problem in graph mining with many applications in diverse domains. At the same time many real-world networks vary over time, that is, the dataset…
Ranking with submodular functions on the fly
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
Maximizing submodular functions have been studied extensively for a wide range of subset-selection problems. However, much less attention has been given to the role of submodularit…
Ranking with submodular functions on a budget
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. How…
Coresets remembered and items forgotten: submodular maximization with deletions
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
In recent years we have witnessed an increase on the development of methods for submodular optimization, which have been motivated by the wide applicability of submodular functions…
Fast computation of distance-generalized cores using sampling
Nikolaj Tatti
Core decomposition is a classic technique for discovering densely connected regions in a graph with large range of applications. Formally, a -core is a maximal subgraph where ea…
Summarizing Data Succinctly with the Most Informative Itemsets
Michael Mampaey, Jilles Vreeken, Nikolaj Tatti
Knowledge discovery from data is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and therefore, what results we would fin…
Tiers for peers: a practical algorithm for discovering hierarchy in weighted networks
Nikolaj Tatti
Interactions in many real-world phenomena can be explained by a strong hierarchical structure. Typically, this structure or ranking is not known; instead we only have observed outc…
Discovering Bands from Graphs
Nikolaj Tatti
Discovering the underlying structure of a given graph is one of the fundamental goals in graph mining. Given a graph, we can often order vertices in a way that neighboring vertices…
Density-friendly Graph Decomposition
Nikolaj Tatti
Decomposing a graph into a hierarchical structure via -core analysis is a standard operation in any modern graph-mining toolkit. -core decomposition is a simple and efficient…
Fast Sequence Segmentation using Log-Linear Models
Nikolaj Tatti
Sequence segmentation is a well-studied problem, where given a sequence of elements, an integer K, and some measure of homogeneity, the task is to split the sequence into K contigu…
Are your Items in Order?
Nikolaj Tatti
Items in many datasets can be arranged to a natural order. Such orders are useful since they can provide new knowledge about the data and may ease further data exploration and visu…
Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles
Nikolaj Tatti, Jilles Vreeken
When analysing binary data, the ease at which one can interpret results is very important. Many existing methods, however, discover either models that are difficult to read, or ret…
The Long and the Short of It: Summarising Event Sequences with Serial Episodes
Nikolaj Tatti, Jilles Vreeken
An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard freque…
Significance of Episodes Based on Minimal Windows
Nikolaj Tatti
Discovering episodes, frequent sets of events from a sequence has been an active field in pattern mining. Traditionally, a level-wise approach is used to discover all frequent epis…
Finding Good Itemsets by Packing Data
Nikolaj Tatti, Jilles Vreeken
The problem of selecting small groups of itemsets that represent the data well has recently gained a lot of attention. We approach the problem by searching for the itemsets that co…
Dynamic hierarchies in temporal directed networks
Nikolaj Tatti
The outcome of interactions in many real-world systems can be often explained by a hierarchy between the participants. Discovering hierarchy from a given directed network can be fo…
Inferring the strength of social ties: a community-driven approach
Polina Rozenshtein, Nikolaj Tatti, Aristides Gionis
Online social networks are growing and becoming denser. The social connections of a given person may have very high variability: from close friends and relatives to acquaintances t…
Discovering bursts revisited: guaranteed optimization of the model parameters
Nikolaj Tatti
One of the classic data mining tasks is to discover bursts, time intervals, where events occur at abnormally high rate. In this paper we revisit Kleinberg's seminal work, where bur…
Discovering Nested Communities
Nikolaj Tatti, Aristides Gionis
Finding communities in graphs is one of the most well-studied problems in data mining and social-network analysis. In many real applications, the underlying graph does not have a c…
Faster way to agony: Discovering hierarchies in directed graphs
Nikolaj Tatti
Many real-world phenomena exhibit strong hierarchical structure. Consequently, in many real-world directed social networks vertices do not play equal role. Instead, vertices form a…
Distances between Data Sets Based on Summary Statistics
Nikolaj Tatti
The concepts of similarity and distance are crucial in data mining. We consider the problem of defining the distance between two data sets by comparing summary statistics computed…
Safe projections of binary data sets
Nikolaj Tatti
Selectivity estimation of a boolean query based on frequent itemsets can be solved by describing the problem by a linear program. However, the number of variables in the equations…
Ranking Episodes using a Partition Model
Nikolaj Tatti
One of the biggest setbacks in traditional frequent pattern mining is that overwhelmingly many of the discovered patterns are redundant. A prototypical example of such redundancy i…
Itemsets for Real-valued Datasets
Nikolaj Tatti
Pattern mining is one of the most well-studied subfields in exploratory data analysis. While there is a significant amount of literature on how to discover and rank itemsets effici…