Add relevant projects for (sub) research directions

Author: RSTZZZ

_config.yml

@@ -54,6 +54,7 @@ plugins:
   - jekyll-gist
   - jemoji
   - jekyll-include-cache
+  - jekyll-redirect-from
 
 author:
   name   : "Complex Data Lab Member(s)"

_includes/sub_research-directions.html

@@ -22,10 +22,14 @@
     {% for direction in sorted_directions %}
     <div class="direction-item">
         <a href="{{ direction.url | relative_url }}" title="{{ direction.title }}">
-            <div class="direction-image" style="background-image: url('{{ direction.header.overlay_image | relative_url }}');
-                   {% if direction.header.overlay_filter %}
-                     background-image: {{ direction.header.overlay_filter }}, url('{{ direction.header.overlay_image | relative_url }}');
-                   {% endif %}">
+            <div class="direction-image" style="
+                   background-image:
+                   {% if direction.header.overlay_filter %} {{ direction.header.overlay_filter }},{% endif %}
+                   url('{{ direction.header.overlay_image | relative_url }}');
+                   {% if direction.header.overlay_size %}
+                   background-size: {{ direction.header.overlay_size }};
+                   {% endif %}
+                   ">
                 <div class="direction-title">
                     <h2>{{ direction.title }}</h2>
                 </div>

_research_directions/poli-sci/fact-checking.md

@@ -7,6 +7,33 @@ header:
     overlay_filter: linear-gradient(rgba(255, 255, 255, 0.1), rgba(0, 0, 0, 0.5))
     overlay_image: /assets/images/research_directions/poli-sci/fact-check.webp
 excerpt: ""
+
+row_intro:
+  - title: ""
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/fact-checking/intro.jpg
+    excerpt: "We aim to explore methods to enhance the accuracy and reliability of online information within the context of digital platforms and AI technologies. By analyzing how information is created, shared, and verified on social media and other digital ecosystems, we aim to develop tools and frameworks that support the identification of misinformation, reduce its spread, and bolster public trust in verified sources. This project contributes to our broader goal of understanding and mitigating the effects of digital misinformation on political discourse and social cohesion."
+
+projects:
+  - title: "Steward AI"
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/fact-checking/steward-ai.jpg
+    excerpt: "The internet is full of dubious information, and it can be difficult and time-consuming to assess the veracity of everything we encounter. In this project, we are building towards an assistant that can help everyone validate information, facilitate learning, avoid harmful manipulation, and help bridge gaps to interact more productively with different groups and cultures."
+  - title: "Misinfo Datasets"
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/fact-checking/misinfo.png
+    excerpt: "We have curated the largest collection of datasets (75) in the literature. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims"
+    url: "https://misinfo-datasets.complexdatalab.com/"
+
+  - title: "Deepfake"
+    alt: "Deepfake"
+    image_path: /assets/images/research_directions/poli-sci/fact-checking/deepfake.jpg
+    excerpt: "Our survey experiment page on the effects of deepfakes."
 ---
 
-Fact-checking!
+{% include feature_row id="row_intro" type="left" %}
+
+# Projects
+{% include feature_row id="projects"%}
+
+<!-- # Funding -->

_research_directions/poli-sci/ideology-and-polarization.md

@@ -7,6 +7,43 @@ header:
     overlay_filter: linear-gradient(rgba(255, 0, 0, 0.5), rgba(0, 0, 255, 1))
     overlay_image: /assets/images/research_directions/poli-sci/social_media.webp
 excerpt: ""
+
+row_intro:
+  - title: ""
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/ideology-and-polarization/intro.jpg
+    excerpt: "Our research on ideology and polarization explores how digital platforms amplify partisan divides and shape political discourse across societal contexts. It focuses on understanding the expression, measurement, and impact of ideological differences in shaping public opinion, both within and across linguistic and cultural boundaries. This area highlights the interplay between technology, political behavior, and societal cohesion in an increasingly interconnected world."
+
+
+project_1:
+  - title: "Party Prediction for Twitter "
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/ideology-and-polarization/party_prediction_for_twitter.jpg
+    excerpt: "Partisanship has increasingly become a major point of contention in public discourse online, and as a result, researchers have developed a variety of methods to evaluate the party affiliations of users on social media. In this paper, we evaluate the performance of party prediction tools and propose new methods that are comparable or improve upon existing works. "
+  - title: "InterPolar"
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/ideology-and-polarization/interpolar.jpg
+    excerpt: "Digital space has become a battleground for partisan debate, threatening the cohesion of societies by facilitating political polarization. In this work, we develop a method for measuring polarization over time using the party affiliations of, and interactions between, social media users, using the 2020 U.S. presidential election as a case study. "
+  - title: "An Evaluation of Language Models for Hyperpartisan Ideology Detection in Persian Twitter"
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/ideology-and-polarization/hyperpartisan_persian.jpg
+    excerpt: "While LLMs have demonstrated how effective they can be for tasks in the English language, such as detecting social media users’ political ideology, their performance in other languages remains understudied. We contribute to this area of research by fine-tuning smaller LLMs to identify hyperpartisans in Persian social media, and compare the results to those from open-source and commercial models."
+
+
+project_2:
+  - title: "Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada"
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/ideology-and-polarization/covid-19.png
+    excerpt: "The COVID-19 pandemic sparked rigorous debate about public health measures online, as the timing and extent of interventions unfolded. This project evaluates partisanship and the geographical distribution of public opinion on lockdowns, masks, and vaccines using the U.S. and Canada as case studies. 
+"
 ---
 
-Welcome to our survey experiment page on the effects of deepfakes. Please check back later to see the results
+{% include feature_row id="row_intro" type="left" %}
+
+# Projects
+
+## Ideology Prediction
+{% include feature_row id="project_1" %}
+
+## Political Polarization
+{% include feature_row id="project_2" type="right" %}

_research_directions/poli-sci/social-simulations.md

@@ -7,6 +7,23 @@ header:
     overlay_filter: linear-gradient(rgba(255, 255, 255, 0.5), rgba(0, 0, 0, 0.7))
     overlay_image: /assets/images/research_directions/poli-sci/social_simulation.webp
 excerpt: ""
+
+row_intro:
+  - title: ""
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/social-simulations/intro.jpg
+    excerpt: "The pernicious effects of digital manipulation campaigns can reverberate through entire societies, but evaluating them in real-world contexts is highly complex and poses ethical challenges. Through this project, we simulate a digital social environment with unprecedented control and study the properties of real world and future manipulation strategies and develop defenses against them."
+
+
+project_1:
+  - title: "A Simulation System Towards Solving Societal-Scale Manipulation"
+    alt: ""
+    image_path: /assets/images/research_directions/poli-sci/social-simulations/social-sim.png
+    excerpt: "First mixed-reality simulation system that can model both online and offline social dynamics"
 ---
 
-Social Simulation!!
+{% include feature_row id="row_intro" type="left" %}
+
+# Publications
+
+{% include feature_row id="project_1" type="right" %}

_research_directions/temporal-graph-learning/TGF.md

@@ -0,0 +1,47 @@
+---
+title: Frontiers
+layout: splash
+category: temporal-graph-learning
+order: 3
+header:
+    overlay_filter: linear-gradient(rgba(255, 255, 255, 0.1), rgba(0, 0, 0, 0.5))
+    overlay_image: /assets/images/research_directions/temporal-graph-learning/TGB.webp
+excerpt: ""
+
+
+row_intro:
+  - title: ""
+    alt: ""
+    image_path: /assets/images/research_directions/temporal-graph-learning/banner.webp
+    excerpt: "Temporal graph foundation models represent a paradigm shift in network science, combining the dynamic expressiveness of temporal graphs with the power of large-scale pre-training to enable sophisticated network understanding and prediction. This emerging field bridges traditional graph learning with modern foundation model approaches, while its integration with LLMs and multi-modal capabilities promises to unlock new frontiers in temporal network analysis."
+
+
+project_1:
+  - title: "Foundation Model"
+    alt: ""
+    image_path: 
+    excerpt: ""
+
+project_2:
+  - title: "+ LLMs"
+    alt: ""
+    image_path: 
+    excerpt: ""
+
+project_3:
+  - title: "Multi-modal"
+    alt: ""
+    image_path: 
+    excerpt: ""
+
+---
+
+{% include feature_row id="row_intro" type="left" %}
+
+<!-- # Projects
+
+{% include feature_row id="project_1" type="right" %}
+{% include feature_row id="project_2" type="left" %}
+{% include feature_row id="project_3" type="right" %}
+
+# Funding -->

_research_directions/temporal-graph-learning/tga.md

@@ -0,0 +1,37 @@
+---
+title: Applications
+layout: splash
+category: temporal-graph-learning
+order: 2
+header:
+    overlay_filter: linear-gradient(rgba(255, 255, 255, 0.1), rgba(0, 0, 0, 0.5))
+    overlay_image: /assets/images/research_directions/temporal-graph-learning/TGB.webp
+excerpt: ""
+
+row_intro:
+  - title: ""
+    alt: ""
+    image_path: /assets/images/research_directions/temporal-graph-learning/banner.webp
+    excerpt: "Temporal graphs provide powerful frameworks for modeling and analyzing real-world systems that evolve over time. In the realm of anomaly detection, these dynamic structures enable us to identify unusual patterns and critical changes in evolving networks, from financial transactions to social interactions. Similarly, in epidemic modeling, temporal graphs capture the crucial time-varying nature of human contact networks and transportation systems, allowing for more accurate predictions of disease spread compared to traditional static network approaches."
+
+project_1:
+  - title: "Anomaly Detection"
+    alt: ""
+    image_path: 
+    excerpt: ""
+
+project_2:
+  - title: "Epidemic Modeling"
+    alt: ""
+    image_path: 
+    excerpt: ""
+---
+
+{% include feature_row id="row_intro" type="left" %}
+
+<!-- # Projects
+
+{% include feature_row id="project_1" type="right" %}
+{% include feature_row id="project_2" type="left" %}
+
+## Funding -->

_research_directions/temporal-graph-learning/tgb.md

@@ -0,0 +1,15 @@
+---
+title: Benchmark
+layout: splash
+category: temporal-graph-learning
+order: 1
+header:
+    overlay_filter: 
+    overlay_image: /assets/images/research_directions/temporal-graph-learning/TGB.jpg
+    overlay_size: "contain"
+excerpt: ""
+
+redirect_to: https://tgb.complexdatalab.com/
+---
+
+