We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network. We zoom in to the network using lenses of different sizes to capture the local structure of the network. These network signatures are then weighted to provide a set of predicted labels for every node. We achieve a peak accuracy of @math (random= @math ) on two networks with @math and @math nodes each. Further, we perform better than random even when the given node is connected to up to 5 different types of networks. Finally, we perform this analysis on homogeneous networks and show that highly structured networks have high homogeneity.
The idea of using the image embedding of the adjacency matrix as a feature was first introduced in @cite_6 . Based on this idea, authors in @cite_7 showed with great success that parent networks of tiny subgraphs (as small as 8 nodes) can be identified. They also used Caffe @cite_8 to show that the structured image embedding features can be used for classification in a transfer learning setting. In this work, we use the idea to create a that can be used on heterogeneous networks to the different exhibited in different parts of a network.
@cite_7 @cite_6 @cite_8
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We propose a novel subgraph image representation for classification of network fragments with the target being their parent networks. The graph image representation is based on 2D image embeddings of adjacency matrices. We use this image representation in two modes. First, as the input to a machine learning algorithm. Second, as the input to a pure transfer learner. Our conclusions from multiple datasets are that 1. deep learning using structured image features performs the best compared to graph kernel and classical features based methods; and, 2. pure transfer learning works effectively with minimum interference from the user and is robust against small data. Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship. To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image text group can be viewed as a class. So the network can learn the fine granularity from every image text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available. We study a natural problem: Given a small piece of a large parent network, is it possible to identify the parent network? We approach this problem from two perspectives. First, using several "sophisticated" or "classical" network features that have been developed over decades of social network study. These features measure aggregate properties of the network and have been found to take on distinctive values for different types of network, at the large scale. By using these classical features within a standard machine learning framework, we show that one can identify large parent networks from small (even 8-node) subgraphs. Second, we present a novel adjacency matrix embedding technique which converts the small piece of the network into an image and, within a deep learning framework, we are able to obtain prediction accuracies upward of 80 , which is comparable to or slightly better than the performance from classical features. Our approach provides a new tool for topology-based prediction which may be of interest in other network settings. Our approach is plug and play, and can be used by non-domain experts. It is an appealing alternative to the often arduous task of creating domain specific features using domain expertise. Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role similarities and other high-order proximities. However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations. In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations. Specifically, we propose an Adversarial Network Embedding (ANE) framework, which leverages the adversarial learning principle to regularize the representation learning. It consists of two components, i.e., a structure preserving component and an adversarial learning component. The former component aims to capture network structural properties, while the latter contributes to learning robust representations by matching the posterior distribution of the latent representations to given priors. As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks.