21 research outputs found
Joint Inference on Truth/Rumor and Their Sources in Social Networks
In the contemporary era of information explosion, we are often faced with the
mixture of massive \emph{truth} (true information) and \emph{rumor} (false
information) flooded over social networks. Under such circumstances, it is very
essential to infer whether each claim (e.g., news, messages) is a truth or a
rumor, and identify their \emph{sources}, i.e., the users who initially spread
those claims. While most prior arts have been dedicated to the two tasks
respectively, this paper aims to offer the joint inference on truth/rumor and
their sources. Our insight is that a joint inference can enhance the mutual
performance on both sides.
To this end, we propose a framework named SourceCR, which alternates between
two modules, i.e., \emph{credibility-reliability training} for truth/rumor
inference and \emph{division-querying} for source detection, in an iterative
manner. To elaborate, the former module performs a simultaneous estimation of
claim credibility and user reliability by virtue of an Expectation Maximization
algorithm, which takes the source reliability outputted from the latter module
as the initial input. Meanwhile, the latter module divides the network into two
different subnetworks labeled via the claim credibility, and in each subnetwork
launches source detection by applying querying of theoretical budget guarantee
to the users selected via the estimated reliability from the former module. The
proposed SourceCR is provably convergent, and algorithmic implementable with
reasonable computational complexity. We empirically validate the effectiveness
of the proposed framework in both synthetic and real datasets, where the joint
inference leads to an up to 35\% accuracy of credibility gain and 29\% source
detection rate gain compared with the separate counterparts
INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion
Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction are
two essential tasks for constructing and applying knowledge graphs. Existing
unsupervised approaches turn out to be suitable candidates for jointly learning
the two tasks due to their avoidance of using graph-text parallel data.
However, they are composed of multiple modules and still require both entity
information and relation type in the training process. To this end, we propose
INFINITY, a simple yet effective unsupervised approach that does not require
external annotation tools or additional parallel information. It achieves fully
unsupervised graph-text mutual conversion for the first time. Specifically,
INFINITY treats both G2T and T2G as a bidirectional sequence generation task by
fine-tuning only one pretrained seq2seq model. A novel back-translation-based
framework is then designed to automatically generate continuous synthetic
parallel data. To obtain reasonable graph sequences with structural information
from source texts, INFINITY employs reward-based training loss by leveraging
the advantage of reward augmented maximum likelihood. As a fully unsupervised
framework, INFINITY is empirically verified to outperform state-of-the-art
baselines for G2T and T2G tasks
Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation
Temporal knowledge graphs, representing the dynamic relationships and
interactions between entities over time, have been identified as a promising
approach for event forecasting. However, a limitation of most temporal
knowledge graph reasoning methods is their heavy reliance on the recurrence or
periodicity of events, which brings challenges to inferring future events
related to entities that lack historical interaction. In fact, the current
state of affairs is often the result of a combination of historical information
and underlying factors that are not directly observable. To this end, we
investigate the limits of historical information for temporal knowledge graph
extrapolation and propose a new event forecasting model called Contrastive
Event Network (CENET) based on a novel training framework of historical
contrastive learning. CENET learns both the historical and non-historical
dependency to distinguish the most potential entities that best match the given
query. Simultaneously, by launching contrastive learning, it trains
representations of queries to probe whether the current moment is more
dependent on historical or non-historical events. These representations further
help train a binary classifier, whose output is a boolean mask, indicating the
related entities in the search space. During the inference process, CENET
employs a mask-based strategy to generate the final results. We evaluate our
proposed model on five benchmark graphs. The results demonstrate that CENET
significantly outperforms all existing methods in most metrics, achieving at
least 8.3% relative improvement of Hits@1 over previous state-of-the-art
baselines on event-based datasets.Comment: Extended version of AAAI paper arXiv:2211.1090
Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
Researchers usually come up with new ideas only after thoroughly
comprehending vast quantities of literature. The difficulty of this procedure
is exacerbated by the fact that the number of academic publications is growing
exponentially. In this study, we devise a framework based on concept
co-occurrence for academic idea inspiration, which has been integrated into a
research assistant system. From our perspective, the fusion of two concepts
that co-occur in an academic paper can be regarded as an important way of the
emergence of a new idea. We construct evolving concept graphs according to the
co-occurrence relationship of concepts from 20 disciplines or topics. Then we
design a temporal link prediction method based on masked language model to
explore potential connections between different concepts. To verbalize the
newly discovered connections, we also utilize the pretrained language model to
generate a description of an idea based on a new data structure called
co-occurrence citation quintuple. We evaluate our proposed system using both
automatic metrics and human assessment. The results demonstrate that our system
has broad prospects and can assist researchers in expediting the process of
discovering new ideas.Comment: Accepted by ACL 202
FMGNN: Fused Manifold Graph Neural Network
Graph representation learning has been widely studied and demonstrated
effectiveness in various graph tasks. Most existing works embed graph data in
the Euclidean space, while recent works extend the embedding models to
hyperbolic or spherical spaces to achieve better performance on graphs with
complex structures, such as hierarchical or ring structures. Fusing the
embedding from different manifolds can further take advantage of the embedding
capabilities over different graph structures. However, existing embedding
fusion methods mostly focus on concatenating or summing up the output
embeddings, without considering interacting and aligning the embeddings of the
same vertices on different manifolds, which can lead to distortion and
impression in the final fusion results. Besides, it is also challenging to fuse
the embeddings of the same vertices from different coordinate systems. In face
of these challenges, we propose the Fused Manifold Graph Neural Network
(FMGNN), a novel GNN architecture that embeds graphs into different Riemannian
manifolds with interaction and alignment among these manifolds during training
and fuses the vertex embeddings through the distances on different manifolds
between vertices and selected landmarks, geometric coresets. Our experiments
demonstrate that FMGNN yields superior performance over strong baselines on the
benchmarks of node classification and link prediction tasks
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Inferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia.
The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis
Macrophages in Glioblastoma Development and Therapy: A Double-Edged Sword
Glioblastoma (GBM) is one of the leading lethal tumors, featuring aggressive malignancy and poor outcome to current standard temozolomide (TMZ) or radio-based therapy. Developing immunotherapies, especially immune checkpoint inhibitors, have improved patient outcomes in other solid tumors but remain fatigued in GBM patients. Emerging evidence has shown that GBM-associated macrophages (GAMs), comprising brain-resident microglia and bone marrow-derived macrophages, act critically in boosting tumor progression, altering drug resistance, and establishing an immunosuppressive environment. Based on its crucial role, evaluations of the safety and efficacy of GAM-targeted therapy are ongoing, with promising (pre)clinical evidence updated. In this review, we summarized updated literature related to GAM nature, the interplay between GAMs and GBM cells, and GAM-targeted therapeutic strategies