Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNode Embedding via Word Embedding for Network Community Discovery
400
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate that the proposed approach consistently improves over the current state-of-the-art. Specifically, our approach empirically attains the information-theoretic limits for community recovery under the benchmark Stochastic Block Models for graph generation and exhibits better stability and accuracy over both Spectral Clustering and Acyclic Belief Propagation in the community recovery limits.
rate research
Read More
Networks such as social networks, airplane networks, and citation networks are ubiquitous. The adjacency matrix is often adopted to represent a network, which is usually high dimensional and sparse. However, to apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while completely neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. Hence, it also has potential to advance network embedding. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model {bf LO}cal and {bf G}lobal information. Experimental results demonstrate the ability to preserve global information of the proposed framework. Further experiments are conducted to demonstrate the effectiveness of learned representations of the proposed framework.
Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art community embedding approach called textit{ComE} tackles this problem by coupling graph embedding with community detection. Considering the success of hyperbolic representations of graph-structured data in last years, an ongoing challenge is to set up a hyperbolic approach for the community detection problem. The present paper meets this challenge by introducing a Riemannian equivalent of textit{ComE}. Our proposed approach combines hyperbolic embeddings with Riemannian K-means or Riemannian mixture models to perform community detection. We illustrate the usefulness of this framework through several experiments on real-world social networks and comparisons with textit{ComE} and recent hyperbolic-based classification approaches.
Considering the wide application of network embedding methods in graph data mining, inspired by the adversarial attack in deep learning, this paper proposes a Genetic Algorithm (GA) based Euclidean Distance Attack strategy (EDA) to attack the network embedding, so as to prevent certain structural information from being discovered. EDA focuses on disturbing the Euclidean distance between a pair of nodes in the embedding space as much as possible through minimal modifications of the network structure. Since a large number of downstream network algorithms, such as community detection and node classification, rely on the Euclidean distance between nodes to evaluate the similarity between them in the embedding space, EDA can be considered as a universal attack on a variety of network algorithms. Different from traditional supervised attack strategies, EDA does not need labeling information, and, in other words, is an unsupervised network embedding attack method.
Dynamic Network Embedding (DNE) has recently attracted considerable attention due to the advantage of network embedding in various applications and the dynamic nature of many real-world networks. For dynamic networks, the degree of changes, i.e., defined as the averaged number of changed edges between consecutive snapshots spanning a dynamic network, could be very different in real-world scenarios. Although quite a few DNE methods have been proposed, it still remains unclear that whether and to what extent the existing DNE methods are robust to the degree of changes, which is however an important factor in both academic research and industrial applications. In this work, we investigate the robustness issue of DNE methods w.r.t. the degree of changes for the first time and accordingly, propose a robust DNE method. Specifically, the proposed method follows the notion of ensembles where the base learner adopts an incremental Skip-Gram neural embedding approach. To further boost the performance, a novel strategy is proposed to enhance the diversity among base learners at each timestep by capturing different levels of local-global topology. Extensive experiments demonstrate the benefits of special designs in the proposed method, and the superior performance of the proposed method compared to state-of-the-art methods. The comparative study also reveals the robustness issue of some DNE methods. The source code is available at https://github.com/houchengbin/SG-EDNE
User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
