Resources for Social Network

博客介绍了社交网络相关内容,推荐了jackyzhong博客上的文章。列举了国外社交产品如linkedin、del.icio.us等,国内的优友地带、圈网中国等,还提及与MSN集成功能。此外,还介绍了无马甲论坛及与email集成的协作文档管理和论坛功能。
关于social network,在jackyzhong的blog上有一篇文章介绍的不错,
http://www.cnblogs.com/jackyzhong/articles/128432.html

国外的产品:

1. www.linkedin.com
Link someone knowen by someone

2. del.icio.us
Link someone known by something 

3. GIS (Geography Infromation System) with Social Software
Example: Trepia, www.trepica.com

国内的产品:

1. 优友地带
www.uuzone.com

2. 圈网中国
www.16play.com
跟MSN集成,可以把MSN上的联系人加入自己的关系网

其他:
1. 没有马甲的论坛
2. 协作-文档管理,与email集成,论坛功能
### Social LSTM Python Code Implementation Example Social LSTM is a model designed to predict human trajectories in crowded spaces by considering social interactions between individuals. A GitHub repository that provides an implementation of this model can be found at the following link[^2]. This project offers comprehensive resources including datasets, training scripts, and evaluation tools. The core components of the Social LSTM architecture include: - **Model Definition**: The neural network structure which incorporates Long Short-Term Memory (LSTM) units. ```python class SocialLSTM(nn.Module): def __init__(self, args): super(SocialLSTM, self).__init__() # Define LSTM layers here based on input arguments def forward(self, obs_traj, neighbors_indices=None): # Implement forward pass logic involving LSTM operations pass ``` - **Data Processing Pipeline**: Preprocessing steps required before feeding data into the model. ```python def process_data(data_file_path): raw_data = load_raw_trajectory_data(data_file_path) processed_data = preprocess(raw_data) return processed_data ``` - **Training Loop**: Script used for training the model with appropriate loss functions and optimizers. ```python for epoch in range(num_epochs): for batch_idx, (input_seq, target_seq) in enumerate(train_loader): optimizer.zero_grad() output = model(input_seq) loss = criterion(output, target_seq) loss.backward() optimizer.step() if batch_idx % log_interval == 0: print(f'Train Epoch: {epoch} [{batch_idx * len(input_seq)}/{len(train_loader.dataset)} ({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}') ``` This particular implementation also includes visualization utilities to help understand how well predictions align with actual movements within crowds[^2].
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