Deep Collaborative Embedding for information cascade prediction
- URL: http://arxiv.org/abs/2001.06665v1
- Date: Sat, 18 Jan 2020 13:32:18 GMT
- Title: Deep Collaborative Embedding for information cascade prediction
- Authors: Yuhui Zhao, Ning Yang, Tao Lin, Philip S. Yu
- Abstract summary: We propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction.
We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration.
The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.
- Score: 58.90540495232209
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, information cascade prediction has attracted increasing interest
from researchers, but it is far from being well solved partly due to the three
defects of the existing works. First, the existing works often assume an
underlying information diffusion model, which is impractical in real world due
to the complexity of information diffusion. Second, the existing works often
ignore the prediction of the infection order, which also plays an important
role in social network analysis. At last, the existing works often depend on
the requirement of underlying diffusion networks which are likely unobservable
in practice. In this paper, we aim at the prediction of both node infection and
infection order without requirement of the knowledge about the underlying
diffusion mechanism and the diffusion network, where the challenges are
two-fold. The first is what cascading characteristics of nodes should be
captured and how to capture them, and the second is that how to model the
non-linear features of nodes in information cascades. To address these
challenges, we propose a novel model called Deep Collaborative Embedding (DCE)
for information cascade prediction, which can capture not only the node
structural property but also two kinds of node cascading characteristics. We
propose an auto-encoder based collaborative embedding framework to learn the
node embeddings with cascade collaboration and node collaboration, in which way
the non-linearity of information cascades can be effectively captured. The
results of extensive experiments conducted on real-world datasets verify the
effectiveness of our approach.
Related papers
- Epidemiology-informed Network for Robust Rumor Detection [59.89351792706995]
We propose a novel Epidemiology-informed Network (EIN) that integrates epidemiological knowledge to enhance performance.
To adapt epidemiology theory to rumor detection, it is expected that each users stance toward the source information will be annotated.
Our experimental results demonstrate that the proposed EIN not only outperforms state-of-the-art methods on real-world datasets but also exhibits enhanced robustness across varying tree depths.
arXiv Detail & Related papers (2024-11-20T00:43:32Z) - Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networks [0.0]
We deploy decoupled GNNs to overcome the issue of capturing nonlinear network information.
For node representation learning, we develop a GNN architecture with two modules for aggregating node feature information.
The findings demonstrate that decoupled training along with the global context enhanced representation of the nodes is superior to the state-of-the-art models in terms of AUC.
arXiv Detail & Related papers (2024-09-04T21:54:07Z) - Hierarchical Information Enhancement Network for Cascade Prediction in Social Networks [51.54002032659713]
We propose a novel Hierarchical Information Enhancement Network (HIENet) for cascade prediction.
Our approach integrates fundamental cascade sequence, user social graphs, and sub-cascade graph into a unified framework.
arXiv Detail & Related papers (2024-03-22T14:57:27Z) - DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery [20.787180028571694]
DiffusionSeg is a synthesis-exploitation framework containing two-stage strategies.
We synthesize abundant images, and propose a novel training-free AttentionCut to obtain masks in the first stage.
In the second exploitation stage, to bridge the structural gap, we use the inversion technique, to map the given image back to diffusion features.
arXiv Detail & Related papers (2023-03-17T07:47:55Z) - Preference Enhanced Social Influence Modeling for Network-Aware Cascade
Prediction [59.221668173521884]
We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling.
Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
arXiv Detail & Related papers (2022-04-18T09:25:06Z) - Improving Information Cascade Modeling by Social Topology and Dual Role
User Dependency [3.3497820777154614]
We propose a non-sequential information cascade model named as TAN-DRUD (Topology-aware Attention Networks with Dual Role User Dependency)
TAN-DRUD obtains satisfactory performance on information cascade modeling through capturing the dual role user dependencies of information sender and receiver.
Our experiments on three cascade datasets demonstrate that our model is not only superior to the state-of-the-art cascade models, but also capable of exploiting topology information and inferring diffusion trees.
arXiv Detail & Related papers (2022-04-07T14:26:33Z) - Independent Asymmetric Embedding Model for Cascade Prediction on Social
Network [0.49269463638915806]
Cascade prediction aims to predict the individuals who will potentially repost the message on the social network.
We propose an independent asymmetric embedding method to learn social embedding for cascade prediction.
arXiv Detail & Related papers (2021-05-18T05:40:38Z) - Unveiling Anomalous Edges and Nominal Connectivity of Attributed
Networks [53.56901624204265]
The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths.
The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance.
The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
arXiv Detail & Related papers (2021-04-17T20:00:40Z) - Joint Inference of Diffusion and Structure in Partially Observed Social
Networks Using Coupled Matrix Factorization [3.399624105745357]
In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks.
The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning and low-dimensional latent factors.
Experiments on these synthetic and real-world datasets show that the proposed method successfully detects invisible social behaviors, predicts links, and identifies latent features.
arXiv Detail & Related papers (2020-10-03T17:48:57Z) - Graph Backdoor [53.70971502299977]
We present GTA, the first backdoor attack on graph neural networks (GNNs)
GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features.
It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
arXiv Detail & Related papers (2020-06-21T19:45:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.