In this paper, we present an interactive approach for shape co-segmentation via label propagation. Our intuitive approach is able to produce error-free results and is very effective at handling out-of-sample data. Specifically, we start by over-segmenting a set of shapes into primitive patches. Then, we allow the users to assign labels to some patches and propagate the label information from th...

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of...

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural network architectures, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training objective for neural networks, Neural Graph Machines, for combining the power of neural networks and label propagation. The new objective allows the neural netw...

We present a graph-based semi-supervised approach for learning user-preferred travel schedules. Assuming a setting in which a user provides a small number of labeled travel schedules, we classify schedules into desirable and non-desirable. This task is non-trivial since only a small number of labeled points is available. It is further complicated by the fact that each schedule is comprised of m...

We present a graph-based semi-supervised approach for learning user-preferred travel schedules. Assuming a setting in which a user provides a small number of labeled travel schedules, we classify schedules into desirable and non-desirable. This task is non-trivial since only a small number of labeled points is available. It is further complicated by the fact that each schedule is comprised of m...

There exist two main approaches to automatically extract affective orientation: lexicon-based and corpus-based. In this work, we argue that these two methods are compatible and show that combining them can improve the accuracy of emotion classifiers. In particular, we introduce a novel variant of the Label Propagation algorithm that is tailored to distributed word representations, we apply batc...

The dynamics of label propagation in a stationary metabolic network during an isotope labeling experiment can provide highly valuable information on the network topology, metabolic fluxes, and on the size of metabolite pools. However, major issues, both in the experimental set-up and in the accompanying numerical methods currently limit the application of this approach. Here, we propose a metho...

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision (DS), which is a method to const...

To overcome the problem of not having enough manually labeled relation instances for supervised relation extraction methods, in this paper we propose a label propagation (LP) based semi-supervised learning algorithm for relation extraction task to learn from both labeled and unlabeled data. Evaluation on the ACE corpus showed when only a few labeled examples are available, our LP based relation...

Despite of the success of convolutional neural networks for semantic image segmentation, CNNs cannot be used for many applications due to limited computational resources. Even efficient approaches based on random forests are not efficient enough for real-time performance in some cases. In this work, we propose an approach based on superpixels and label propagation that reduces the runtime of a ...