52,455 research outputs found
Stealing Links from Graph Neural Networks
Graph data, such as chemical networks and social networks, may be deemed
confidential/private because the data owner often spends lots of resources
collecting the data or the data contains sensitive information, e.g., social
relationships. Recently, neural networks were extended to graph data, which are
known as graph neural networks (GNNs). Due to their superior performance, GNNs
have many applications, such as healthcare analytics, recommender systems, and
fraud detection. In this work, we propose the first attacks to steal a graph
from the outputs of a GNN model that is trained on the graph. Specifically,
given a black-box access to a GNN model, our attacks can infer whether there
exists a link between any pair of nodes in the graph used to train the model.
We call our attacks link stealing attacks. We propose a threat model to
systematically characterize an adversary's background knowledge along three
dimensions which in total leads to a comprehensive taxonomy of 8 different link
stealing attacks. We propose multiple novel methods to realize these 8 attacks.
Extensive experiments on 8 real-world datasets show that our attacks are
effective at stealing links, e.g., AUC (area under the ROC curve) is above 0.95
in multiple cases. Our results indicate that the outputs of a GNN model reveal
rich information about the structure of the graph used to train the model.Comment: To appear in the 30th Usenix Security Symposium, August 2021,
Vancouver, B.C., Canad
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Structure of Heterogeneous Networks
Heterogeneous networks play a key role in the evolution of communities and
the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure
Neural Collective Entity Linking
Entity Linking aims to link entity mentions in texts to knowledge bases, and
neural models have achieved recent success in this task. However, most existing
methods rely on local contexts to resolve entities independently, which may
usually fail due to the data sparsity of local information. To address this
issue, we propose a novel neural model for collective entity linking, named as
NCEL. NCEL applies Graph Convolutional Network to integrate both local
contextual features and global coherence information for entity linking. To
improve the computation efficiency, we approximately perform graph convolution
on a subgraph of adjacent entity mentions instead of those in the entire text.
We further introduce an attention scheme to improve the robustness of NCEL to
data noise and train the model on Wikipedia hyperlinks to avoid overfitting and
domain bias. In experiments, we evaluate NCEL on five publicly available
datasets to verify the linking performance as well as generalization ability.
We also conduct an extensive analysis of time complexity, the impact of key
modules, and qualitative results, which demonstrate the effectiveness and
efficiency of our proposed method.Comment: 12 pages, 3 figures, COLING201
A semantic-based platform for the digital analysis of architectural heritage
This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings
Automatic Synchronization of Multi-User Photo Galleries
In this paper we address the issue of photo galleries synchronization, where
pictures related to the same event are collected by different users. Existing
solutions to address the problem are usually based on unrealistic assumptions,
like time consistency across photo galleries, and often heavily rely on
heuristics, limiting therefore the applicability to real-world scenarios. We
propose a solution that achieves better generalization performance for the
synchronization task compared to the available literature. The method is
characterized by three stages: at first, deep convolutional neural network
features are used to assess the visual similarity among the photos; then, pairs
of similar photos are detected across different galleries and used to construct
a graph; eventually, a probabilistic graphical model is used to estimate the
temporal offset of each pair of galleries, by traversing the minimum spanning
tree extracted from this graph. The experimental evaluation is conducted on
four publicly available datasets covering different types of events,
demonstrating the strength of our proposed method. A thorough discussion of the
obtained results is provided for a critical assessment of the quality in
synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi
- …