Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II
Book and Conference Proceedings
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II
Book and Conference Proceedings
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part I
Book and Conference Proceedings
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV
Book and Conference Proceedings
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III
Book and Conference Proceedings
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V
Article
The traditional setup of link prediction in networks assumes that a test set of node pairs, which is usually balanced, is available over which to predict the presence of links. However, in practice, there is n...
Chapter and Conference Paper
How can we effectively detect fake reviews or fraudulent connections on a website? How can we spot communities that suddenly appear based on users’ interaction? And how can we efficiently find the minimum cut ...
Article
Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream ...
Chapter and Conference Paper
Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalizatio...
Article
Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, climate science, economics, and more. In domains where networks are discover...
Article
The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent y...
Article
The rapid growth of social networks and their strong presence in our lives have attracted many researchers in social networks analysis. Users of social networks spread their opinions, get involved in discussio...
Article
Graphs, such as social, road and information networks, are ubiquitous as they naturally model entities and their relationships. Many query processing tasks on graphs are concerned about efficiently accessing n...
Article
Summarizing a large graph with a much smaller graph is critical for applications like speeding up intensive graph algorithms and interactive visualization. In this paper, we propose CONditional Diversified Net...
Chapter
Chapter
Graphs are very powerful representations of data and the relations among them. The Web, friendships and communications, collaborations and phone calls, traffic flow, or brain functions are only few examples of...
Chapter
One natural way to understand a graph and its underlying processes is to visualize and interact with it. However, for large datasets with several millions or billions of nodes and edges, such as the Facebook s...
Chapter
In many applications, it is necessary or at least beneficial to explore multiple graphs collectively. These graphs can be temporal instances of the same set of objects (time-evolving graphs), or disparate netw...
Book
Chapter
Can we spot the same people in two different social networks, such as LinkedIn and Facebook? How can we find similar people across different graphs? How can we effectively link an information network with a socia...