**Contents:**

The experimental results demonstrate the effectiveness of learning higher-order network representations. Higher-order network embeddings Graphlets Network motifs Induced subgraph patterns More Orbits Higher-order network analysis Network representation learning Graph feature learning Graph representation learning Node embeddings Higher-order graph features Inductive graph features Attributed graphs Feature diffusion Higher-order graph embeddings Higher-order organization Higher-order network modules Higher-order connectivity patterns HONE.

This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large attributed graphs. In particular, DeepGL begins by deriving a set of base features e. Contrary to previous work, DeepGL learns relational functions each representing a feature that generalize across-networks and therefore useful for graph-based transfer learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns interpretable graph representations, and is space-efficient by learning sparse feature vectors.

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Inductive network representation learning Inductive learning Network representation learning Graph feature learning More Random walks are at the heart of many existing network embedding methods. However, such methods have many limitations that arise from the use of traditional random walks, e. In this More Our proposed framework enables these methods to be more widely applicable by learning functions that capture the behavioral roles of the nodes.

We show that our proposed framework is effective with an average AUC improvement of Feature-based walk Attributed walk Roles Positions More Network representation learning Node embeddings Graph embeddings Role-based embeddings Role discovery Inductive representation learning Attributed networks Graphlets Attributed random walks Labeled random walks.

Ahmed and Ryan A. Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks.

Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms, however, it is often too expensive to More This is vastly more accurate than existing methods while using less data.

These are by far the largest graphlet computations to date. Graphlets Network motifs Induced subgraphs Estimation methods More Unbiased graphlet estimation Local graphlet count estimation Graphlet statistics Parallel algorithms Higher-order network analysis Machine learning. Dynamic network embeddings Temporal network embeddings Continious-time dynamic network embeddings Inductive network representation learning More Network representation learning Graph representation learning Node embeddings Dynamic networks Temporal networks Graph streams Continious-time dynamic networks.

However, such algorithms have many limitations that arise from the use of random walks, e. In this work, we introduce the Role2Vec framework which uses the flexible notion of attributed random walks, and serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes if available.

This is achieved by learning functions that generalize to new nodes and graphs. Network representation learning Node embeddings Graph embeddings Role-based embeddings More Role discovery Inductive representation learning Attributed networks Graphlets Attributed random walks Labeled random walks.

Orbits Higher-order network analysis Network representation learning Graph feature learning Graph representation learning Node embeddings Higher-order graph features Inductive graph features Attributed graphs Feature diffusion Higher-order graph embeddings Higher-order organization Higher-order network modules Higher-order connectivity patterns HONE Deep learning.

Complex networks are often categorized according to the underlying phenomena that they represent such as molecular interactions, re-tweets, and brain activity. In this work, we investigate the problem of predicting the category domain of arbitrary networks.

This includes complex networks from different domains as well as synthetically generated graphs More A classification accuracy of This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network.

Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the network model used to generate it. Overall, the results demonstrate that networks drawn from different domains and network models are trivial to distinguish using only a handful of simple structural properties. Network classification Network categorization Graph classification Graph features More Massive graphs Big data Massive networks Machine learning Structural properties Across-domain graph classification Network science Complex networks Graph similarity Graph matching.

Canning and Emma E. Ortiz and Nesreen K. Rossi and Karl R. Massive graphs are ubiquitous and at the heart of many real-world applications ranging from the World Wide Web to social networks. As a result, techniques for compressing graphs have become increasingly important.

Patent Di Jin , Ryan A. According to the system and the method, at least one matrix is received. Richard M. Finally, the space of temporal-relational models are evaluated using a sample of classifiers. This dissertation investigates the problem of Relational Time-series Learning from dynamic attributed graph data, with the goal of improving the predictive quality of existing RML methods. Second, they are not space-efficient as a feature vector is learned for each node which is impractical for large graphs. However, such algorithms have many limitations that arise from the use of random walks, e.

In this work, we propose a graph compression and encoding framework called GraphZIP based on the observation More Using this as a foundation, we decompose the graph into a set of large cliques, which is then used to encode the graph succinctly. In particular, disk-resident and in-memory graph encodings are proposed and shown to be effective with three important benefits. First, it reduces the space needed to store the graph on disk and in-memory. Third, it can often reduce the work involved in running an algorithm on the graph.

The experiments demonstrate the scalability, flexibility, and effectiveness of the clique-based compression techniques. Networks encode dependencies between entities people, computers, proteins and allow us to study phenomena across social, technological, and biological domains. These networks naturally evolve over time by the addition, deletion, and changing of links, nodes, and attributes.

Despite the importance of modeling these dynamics, existing work in relational machine More Relational time series learning lies at the intersection of traditional time series analysis and Statistical Relational Learning SRL , and bridges the gap between these two fundamentally important problems.

This paper formulates the relational time series learning problem, and a general framework and taxonomy for representation discovery tasks of both nodes and links including predicting their existence, label, and weight importance , as well as systematically constructing features. We also reinterpret the prediction task leading to the proposal of two important relational time series forecasting tasks consisting of i relational time series classification predicts a future class or label of an entity , and ii relational time series regression predicts a future real-valued attribute or weight.

Relational time series models are designed to leverage both relational and temporal dependencies to minimize forecasting error for both relational time series classification and regression. Finally, we discuss challenges and open problems that remain to be addressed. This paper presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a More Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data.

In particular, we propose techniques for interactive relational learning e. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators including new block model approaches , and a variety of multi-level network analysis techniques. Interactive relational machine learning Interactive visual graph mining Interactive network analysis Interactive network visualization More Interactive graph learning Higher-order network analysis Interactive role discovery Link prediction Node embeddings Interactive graph generation Rapid visual feedback Direct manipulation Real-time performance.

Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classification, anomaly detection, link prediction, among many others. Many existing techniques use random walks as a basis for learning features or estimating the parameters of a graph model More Examples include recent node embedding methods such as DeepWalk, node2vec, as well as graph-based deep learning algorithms. However, the simple random walk used by these methods is fundamentally tied to the identity of the node.

This has three main disadvantages. First, these approaches are inherently transductive and do not generalize to unseen nodes and other graphs. Second, they are not space-efficient as a feature vector is learned for each node which is impractical for large graphs.

Third, most of these approaches lack support for attributed graphs. This framework serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many other previous methods that leverage traditional random walks. Network representation learning Roles Structural similarity Graph embeddings More Feature learning Relational functions Random walks Deep learning Graph-based deep learning Machine learning Network embedding Inductive representation learning.

Most of the existing methods take the entire graph into account when calculating graph features. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or sub-graphs. In this work, we study the problem of attentional processing for graph classification.

On. Deals with graph based semi supervised learning methods. Text categorization through locating the thesis title: Is the problem of designing a bipartite. This thesis focuses on classification, which is traditionally a supervised learn- semi-supervised learning includes regression and clustering as well, but we will.

We present a novel RNN model, called the Graph Attention Model GAM , that processes only a portion of the graph by adaptively selecting a sequence of "interesting" nodes. We demonstrate the effectiveness of the model through various experiments. Graph attention Attention model Deep graph models Reinforcement learning More Recurrent neural networks Graph representation Representation learning Graph embeddings Graph feature learning Graph classification Bioinformatics Deep learning Graph-based deep learning Machine learning.

Random walks are at the heart of many existing deep learning algorithms for graph data. In this work, we introduce the notion of attributed random walks which serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework makes these methods more widely applicable for both transductive and inductive learning by learning functions that generalize to new nodes and graphs as well as for use on graphs with attributes if available.

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Finally, the approach is shown to be effective with an average AUC improvement of Graph representation Representation learning Graph embeddings Feature learning More Relational functions Random walks Network embedding Inductive representation learning Inductive learning Attributed networks Attributed network representation learning Deep learning Graph-based deep learning Machine learning. Relational functions Random walks Deep learning Network embedding Inductive representation learning Inductive learning Attributed networks Attributed network representation learning Graph-based deep learning Machine learning.

To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories e. First, real-world networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of an arbitrary network. Second, classifying synthetic networks is trivial as our models can easily distinguish between synthetic graphs and the real-world networks they are supposed to model.

Massive graphs Big data Massive networks Machine learning. Conference Nesreen K. We propose Graph Priority Sampling GPS , a new paradigm for order-based reservoir sampling from massive streams of graph edges. In the context of subgraph More In distinction with many prior graph sampling schemes, GPS separates the functions of edge sampling and subgraph estimation.