6 research outputs found
Topological based classification of paper domains using graph convolutional networks
The main approaches for node classification in graphs are information
propagation and the association of the class of the node with external
information. State of the art methods merge these approaches through Graph
Convolutional Networks. We here use the association of topological features of
the nodes with their class to predict this class. Moreover, combining
topological information with information propagation improves classification
accuracy on the standard CiteSeer and Cora paper classification task.
Topological features and information propagation produce results almost as good
as text-based classification, without no textual or content information. We
propose to represent the topology and information propagation through a GCN
with the neighboring training node classification as an input and the current
node classification as output. Such a formalism outperforms state of the art
methods
A combined network and machine learning approaches for product market forecasting
Sustainable financial markets play an important role in the functioning of
human society. Still, the detection and prediction of risk in financial markets
remain challenging and draw much attention from the scientific community. Here
we develop a new approach based on combined network theory and machine learning
to study the structure and operations of financial product markets. Our network
links are based on the similarity of firms' products and are constructed using
the Securities Exchange Commission (SEC) filings of US listed firms. We find
that several features in our network can serve as good precursors of financial
market risks. We then combine the network topology and machine learning methods
to predict both successful and failed firms. We find that the forecasts made
using our method are much better than other well-known regression techniques.
The framework presented here not only facilitates the prediction of financial
markets but also provides insight and demonstrate the power of combining
network theory and machine learning
Regional based query in graph active learning
Graph convolution networks (GCN) have emerged as the leading method to
classify node classes in networks, and have reached the highest accuracy in
multiple node classification tasks. In the absence of available tagged samples,
active learning methods have been developed to obtain the highest accuracy
using the minimal number of queries to an oracle. The current best active
learning methods use the sample class uncertainty as selection criteria.
However, in graph based classification, the class of each node is often related
to the class of its neighbors. As such, the uncertainty in the class of a
node's neighbor may be a more appropriate selection criterion. We here propose
two such criteria, one extending the classical uncertainty measure, and the
other extending the page-rank algorithm. We show that the latter is optimal
when the fraction of tagged nodes is low, and when this fraction grows to one
over the average degree, the regional uncertainty performs better than all
existing methods. While we have tested this methods on graphs, such methods can
be extended to any classification problem, where a distance metrics can be
defined between the input samples.
All the code used can be accessed at : https://github.com/louzounlab/graph-al
All the datasets used can be accessed at :
https://github.com/louzounlab/DataSetsComment: 9 pages, 7 figure
Topological based classification using graph convolutional networks
In colored graphs, node classes are often associated with either their
neighbors class or with information not incorporated in the graph associated
with each node. We here propose that node classes are also associated with
topological features of the nodes. We use this association to improve Graph
machine learning in general and specifically, Graph Convolutional Networks
(GCN).
First, we show that even in the absence of any external information on nodes,
a good accuracy can be obtained on the prediction of the node class using
either topological features, or using the neighbors class as an input to a GCN.
This accuracy is slightly less than the one that can be obtained using content
based GCN.
Secondly, we show that explicitly adding the topology as an input to the GCN
does not improve the accuracy when combined with external information on nodes.
However, adding an additional adjacency matrix with edges between distant nodes
with similar topology to the GCN does significantly improve its accuracy,
leading to results better than all state of the art methods in multiple
datasets.Comment: arXiv admin note: text overlap with arXiv:1904.0778
Exposing individual differences through network topology
Social animals, including humans, have a broad range of personality traits,
which can be used to predict individual behavioral responses and decisions.
Current methods to quantify individual personality traits in humans rely on
self-report questionnaires, which require time and effort to collect and rely
on active cooperation. However, personality differences naturally manifest in
social interactions such as online social networks. Here, we demonstrate that
the topology of an online social network can be used to characterize the
personality traits of its members. We analyzed the directed social graph formed
by the users of the LiveJournal (LJ) blogging platform. Individual users
personality traits, inferred from their self-reported domains of interest
(DOIs), were associated with their network measures. Empirical clustering of
DOIs by topological similarity exposed two main self-emergent DOI groups that
were in alignment with the personality meta-traits plasticity and stability.
Closeness, a global topological measure of network centrality, was
significantly higher for bloggers associated with plasticity (vs. stability). A
local network motif (a triad of 3 connected bloggers) that correlated with
closeness also separated the personality meta-traits. Finally, topology-based
classification of DOIs (without analyzing the content of the blogs) attained >
70% accuracy (average AUC of the test-set). These results indicate that
personality traits are evident and detectable in network topology. This has
serious implications for user privacy. But, if used responsibly, network
identification of personality traits could aid in early identification of
health-related risks, at the population level
Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis
Many real-world relations can be represented by signed networks with positive
links (e.g., friendships and trust) and negative links (e.g., foes and
distrust). Link prediction helps advance tasks in social network analysis such
as recommendation systems. Most existing work on link analysis focuses on
unsigned social networks. The existence of negative links piques research
interests in investigating whether properties and principles of signed networks
differ from those of unsigned networks, and mandates dedicated efforts on link
analysis for signed social networks. Recent findings suggest that properties of
signed networks substantially differ from those of unsigned networks and
negative links can be of significant help in signed link analysis in
complementary ways. In this article, we center our discussion on a challenging
problem of signed link analysis. Signed link analysis faces the problem of data
sparsity, i.e. only a small percentage of signed links are given. This problem
can even get worse when negative links are much sparser than positive ones as
users are inclined more towards positive disposition rather than negative. We
investigate how we can take advantage of other sources of information for
signed link analysis. This research is mainly guided by three social science
theories, Emotional Information, Diffusion of Innovations, and Individual
Personality. Guided by these, we extract three categories of related features
and leverage them for signed link analysis. Experiments show the significance
of the features gleaned from social theories for signed link prediction and
addressing the data sparsity challenge.Comment: This worked is published at ACM Transactions on Intelligent Systems
and Technology(ACM TIST), 201