4 research outputs found
Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators
Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks
Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection
Community detection is an essential task in network analysis as it helps
identify groups and patterns within a network. High-speed community detection
algorithms are necessary to analyze large-scale networks in a reasonable amount
of time. Researchers have made significant contributions in the development of
high-speed community detection algorithms, particularly in the area of
label-propagation based disjoint community detection. These algorithms have
been proven to be highly effective in analyzing large-scale networks in a
reasonable amount of time. However, it is important to evaluate the performance
and accuracy of these existing methods to determine which algorithm is best
suited for a particular type of network and specific research problem. In this
report, we investigate the RAK, COPRA, and SLPA, three label-propagation-based
static community discovery techniques. We pay close attention to each
algorithm's minute details as we implement both its single-threaded and
multi-threaded OpenMP-based variants, making any necessary adjustments or
optimizations and obtaining the right parameter values. The RAK algorithm is
found to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with
12 threads was 6.75x faster than the sequential non-strict RAK. The COPRA
algorithm works well with a single label for road networks and max labels of
4-16 for other classes of graphs. The SLPA algorithm performs well with
increasing memory size, but overall doesn't offer a favourable return on
investment. The RAK algorithm is recommended for label-propagation based
disjoint community detection.Comment: 11 pages, 1 tabl